Documentation
This section provides examples of how to use the SPMF open-source data mining library to perform various data mining tasks. If you have any question or if you want to report a bug,you can check the FAQ,post in the forum or contact me.You can also have a look at the various articles that I have referenced on the algorithms page of this website to learn more about each algorithm. Moreover, you can have a look at the page about videos and other resources related to SPMF, including a textbook in Thai language.Itemset Mining (Frequent Itemsets, Rare Itemsets, etc.)
- Example : Mining Frequent Itemsets by Using the Apriori Algorithm
- Example 1 : Mining Frequent Itemsets by Using the AprioriTID Algorithm
- Example 2 : Mining Frequent Itemsets by Using the FP-Growth Algorithm
- Example 3 : Mining Frequent Itemsets by Using the Relim Algorithm
- Example 4 : Mining Frequent Itemsets by Using the Eclat / dEclat Algorithm
- Example 5 : Mining Frequent Itemsets by Using the H-Mine Algorithm
- Example 6 : Mining Frequent Itemsets by Using the FIN Algorithm
- Example 7 : Mining Frequent Itemsets by Using the DFIN Algorithm
- Example 8 : Mining Frequent Itemsets by Using the NegFIN Algorithm
- Example 9 : Mining Frequent Itemsets by Using the PrePost / PrePost+ Algorithm
- Example 10 : Mining Frequent Itemsets by Using the LCMFreq Algorithm
- Example 11 : Mining Frequent Itemsets by Using the DIC Algorithm
- Example 12 : Mining Frequent Itemsets by Using the TM Algorithm
- Example 13 : Mining Frequent Itemsets by Using the LinearTable Algorithm
- Example 14 : Mining Frequent Itemsets by Using the SAM Algorithm
- Example 15 : Mining Frequent Closed Itemsets Using the AprioriClose or AprioriTIDClose Algorithms
- Example 16 : Mining Frequent Closed Itemsets Using the DCI_Closed Algorithm
- Example 17 : Mining Frequent Closed Itemsets Using the Charm / dCharm Algorithm
- Example 18 : Mining Frequent Closed Itemsets Using the LCM Algorithm
- Example 19 : Mining Frequent Closed Itemsets Using the FPClose Algorithm
- Example 20 : Mining Frequent Closed Itemsets Using the NAFCP Algorithm
- Example 21 : Mining Frequent Closed Itemsets Using the NEclatClosed Algorithm
- Example 22 : Mining Frequent Closed Itemsets Using the CARPENTER Algorithm
- Example 23 : Mining Frequent Closed Itemsets Using the NEWCharm Algorithm
- Example 24 : Mining Frequent Closed Itemsets Using the DBVMiner Algorithm
- Example 25 : Mining Frequent Maximal Itemsets Using the FPMax Algorithm
- Example 26 : Mining Frequent Maximal Itemsets Using the Charm-MFI Algorithm
- Example 27 : Mining Frequent Closed Itemsets Using the CARPENTER-MAX Algorithm
- Example 28 : Mining Frequent Maximal Itemsets Using the GENMAX Algorithm
- Example 29 : Mining Frequent Generator Itemsets Using the DefMe Algorithm
- Example 30 : Mining Frequent Generator Itemsets Using the TalkyG Algorithm
- Example 31 : Mining Frequent Itemsets and Identify the Generators Using the Pascal Algorithm
- Example 32 : Mining Frequent Closed Itemsets and Minimal Generators Using the Zart Algorithm
- Example 33 : Mining Minimal Rare Itemsets Using the AprioriRare or AprioriTIDRare Algorithms
- Example 34 : Mining Perfectly Rare Itemsets Using the AprioriInverse or AprioriTIDInverse Algorithms
- Example 35 : Mining Rare Correlated Itemsets Using the CORI Algorithm
- Example 36 : Mining Rare Itemsets Using the RP-Growth Algorithm
- Example 37 : Mining Closed Itemsets from a Data Stream Using the CloStream Algorithm (source code version only)
- Example 38 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec Algorithm (source code version only)
- Example 39 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec+ Algorithm (source code version only)
- Example 40 : Mining Frequent Itemsets from Uncertain Data with the UApriori Algorithm
- Example 41 : Mining Frequent Itemsets from Uncertain Data with the UVEclat Algorithm
- Example 42 : Mining Frequent Itemsets from Uncertain Data with the UH-Mine Algorithm
- Example 43 : Mining the Top-K Frequent Itemsets from Uncertain Data with the TUFP Algorithm
- Example 44 : Mining Erasable Itemsets from a Product Database with the VME algorithm
- Example 45 : Mining Erasable Itemsets from a Product Database with the META algorithm
- Example 46 : Mining Erasable Itemsets from a Product Database with the MEI algorithm
- Example 47 : Mining Erasable Itemsets from a Product Database with the MERIT+ algorithm
- Example 48 : Mining Erasable Itemsets from a Product Database with the dMERIT+ algorithm
- Example 49 : Building, updating incrementally and using an Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
- Example 50 : Building, updating incrementally and using a Memory-Efficient Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
- Example 51 : Mining Frequent Itemsets with Multiple Support Thresholds Using the MSApriori Algorithm
- Example 52 : Mining Frequent Itemsets with Multiple Support Thresholds Using the CFPGrowth++ Algorithm
- Example 53 : Mining Fuzzy Frequent Itemsets in a quantitative transaction database using the FFI-Miner algorithm
- Example 54 : Mining Multiple Fuzzy Frequent Itemsets in a quantitatve transaction database using the MFFI-Miner algorithm
- Example 55 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the LevelWise algorithm
- Example 56 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the DFI-Growth algorithm
- Example 57 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the DFI-List algorithm
- Example 58 : Mining Self-Sufficient Itemsets using the Opus-Miner algorithm
- Example 59 : Mining the Top-K Frequent Itemsets by Using the Apriori(top-k) Algorithm
- Example 60 : Mining the Top-K Frequent Itemsets by Using the FP-Growth(top-k) Algorithm
- Example 61 : Mining the Top-K Frequent Itemsets by Using the HTK_Miner Algorithm
- Example 62 : Mining the Top-K Frequent Itemsets by Using the HTK_negFIN Algorithm
- Example 63 : Mining Compressing Itemsets using the Krimp Algorithm
- Example 64 : Mining Compressing Itemsets using the SLIM Algorithm
- Example 65 : Mining Compressing Itemsets using the GRIMP Algorithm
- Example 66 : Mining Compressing Itemsets using the HMP-SA Algorithm
- Example 67 : Mining Compressing Itemsets using the HMP-HC Algorithm
- Example 68 : Mining Frequent Weighted Itemsets using the WFIM Algorithm
- Example 69 : Mining Frequent Weighted Itemsets using the WIT-FWI, WIT-FWI-MOD or WIT-FWI-DIFF Algorithms
- Example 70 : Mining Frequent Weighted Itemsets using the NFWI Algorithm
- Example 71 : Mining Frequent Weighted Itemsets using the NFWCI Algorithm
High-Utility Pattern Mining
- Example 72 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the Two-Phase Algorithm
- Example 73 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the FHM Algorithm
- Example 74 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the EFIM Algorithm
- Example 75 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUI-Miner Algorithm
- Example 76 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUP-Miner Algorithm
- Example 77 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UP-Growth / UP-Growth+ Algorithm
- Example 78 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the IHUP Algorithm
- Example 79 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the mHUIMiner Algorithm
- Example 80 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HMiner Algorithm
- Example 81 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUIM-SU Algorithm
- Example 82 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the ULB-Miner Algorithm
- Example 83 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UFH Algorithm
- Example 84 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the d2HUP Algorithm
- Example 85 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the FHIM Algorithm
- Example 86 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UPHist Algorithm
- Example 87 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the MEHUIM Algorithm
- Example 88 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the RMiner Algorithm
- Example 89 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the ScentedUtilityMiner Algorithm
- Example 90 : Mining High-Utility Itemsets from a Transaction Database with Utility Information while considering Length Constraints, using the FHM+ algorithm
- Example 91 : Mining Cross-Level High-Utility Itemsets from a Transaction Database with Utility Information using the CLH-Miner algorithm
- Example 92: Mining Cross-Level High-Utility Itemsets from a Transaction Database with Utility Information using the FEACP algorithm
- Example 93 : Mining the Multi-Level High-Utility Itemsets from a Transaction Database with Utility Information using the MLHUI-Miner algorithm
- Example 94 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the FCHM_bond algorithm
- Example 95 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the FCHM_allconfidence algorithm
- Example 96 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the ECHUM algorithm
- Example 97 : Mining Frequent High-Utility Itemsets from a Transaction Database with Utility Information using the FHMFreq Algorithm
- Example 98 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the FHN Algorithm
- Example 99 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the HUINIV-Mine Algorithm
- Example 100 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the FOSHU Algorithm
- Example 101 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the TS-HOUN Algorithm
- Example 102 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the EIHI Algorithm (source code version only)
- Example 103 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the HUI-LIST-INS Algorithm (source code version only)
- Example 104 : Incremental Closed High-Utility Itemset Mining in a Transaction Database with utility information using the INCCHUI Algorithm (source code version only)
- Example 105 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the EFIM-Closed Algorithm
- Example 106 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUI-Miner Algorithm
- Example 107 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUI-Miner(Max) Algorithm
- Example 108 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUD Algorithm
- Example 109 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CLS-Miner Algorithm
- Example 110 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the HMiner-Closed Algorithm
- Example 111 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the EFIM-Closed Algorithm
- Example 112 : Mining Generators of High-Utility Itemsets from a transaction database with utility information using the GHUI-Miner Algorithm
- Example 113 : Mining High-Utility Generator Itemsets from a transaction database with utility information using the HUG-Miner Algorithm
- Example 114: Mining Closed High Utility Itemsets and High-Utility Generator Itemsets from a transaction database with utility information using the HUCI_Miner Algorithm
- Example 115 : Mining Minimal High-Utility Itemsets from a transaction database with utility information using the MinFHM Algorithm
- Example 116 : Mining Skyline High-Utility Itemsets in a transaction database with utility information using the SkyMine Algorithm
- Example 117 : Mining Skyline Frequent High-Utility Itemsets in a transaction database with utility information using the SFUI_UF Algorithm
- Example 118 : Mining Skyline Frequent High-Utility Itemsets in a transaction database with utility information using the SFU-CE Algorithm
- Example 119 : Mining High-Utility Sequential Rules from a Sequence Database with utility information using the HUSRM Algorithm
- Example 120 : Mining High-Utility Sequential Patterns from a Sequence Database with utility information using the USPAN Algorithm
- Example 121 : Mining High-Utility Probability Sequential Patterns from a Sequence Database with utility and probability information using the PHUSPM Algorithm
- Example 122 : Mining High-Utility Probability Sequential Patterns from a Sequence Database with utility information and probability using the UHUSPM Algorithm
- Example 123 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO algorithm
- Example 124 : Mining High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA algorithm
- Example 125 : Mining High Utility Itemsets Using Ant Colony Optimization Algorithm with the HUIM-ACO algorithm
- Example 126 : Mining High Utility Itemsets Using Hill-Climbing with the HUIM-HC algorithm
- Example 127 : Mining High Utility Itemsets Using Simulated Annealing with the HUIM-SA algorithm
- Example 128 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO-tree algorithm
- Example 129 : Discovery of High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA-tree algorithm
- Example 130 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIF-PSO algorithm
- Example 131 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-SPSO algorithm
- Example 132 : Mining High-Utility Itemsets Using a Genetic Algorithm with the HUIF-GA algorithm
- Example 133 : Mining High-Utility Itemsets Using a Bat Algorithm with the HUIF-BA algorithm
- Example 134 : Mining High-Utility Itemsets Using Artificial Fish Swarm Algorithm with the HUIM-AF algorithm
- Example 135 : Mining High-Utility Itemsets Using a Artificial Bee Colony Algorithm with the HUIF-ABC algorithm
- Example 136 : Mining Skyline Frequent-Utility Patterns using the SFUPMinerUemax algorithm
- Example 137 : Mining Skyline Frequent-Utility Patterns using the EMSFUI_B algorithm
- Example 138 : Mining Skyline Frequent-Utility Patterns using the EMSFUI_D algorithm
- Example 139 : Mining the Top-k high-utility itemsets using the TKU algorithm
- Example 140 : Mining the Top-k high-utility itemsets using the TKO (basic) algorithm
- Example 141 : Mining the Top-k high-utility itemsets using the THUI algorithm
- Example 142 : Mining the Top-k high-utility itemsets using the TKU-CE algorithm
- Example 143 : Mining the Top-k high-utility itemsets using the TKU-CE+ algorithm
- Example 144 : Mining the Top-k high-utility itemsets in a data stream using the FHMDS algorithm
- Example 145 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the HAUI-Miner Algorithm
- Example 146 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the EHAUPM Algorithm
- Example 147 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the HAUIM-GMU Algorithm
- Example 148 : Mining High Average-Utility Itemsets with Multiple Thresholds in a Transaction Database using the HAUI-MMAU Algorithm
- Example 149 : Mining High Average-Utility Itemsets with Multiple Thresholds in a Transaction Database using the MEMU Algorithm
- Example 150 : Mining the Top-K High Average-Utility Itemsets in a Transaction Database using the ETAUIM Algorithm
- Example 151 : Mining Quantitative High Utility Itemsets in a Transaction Database using the VHUQI Algorithm
- Example 152 : Mining Quantitative High Utility Itemsets in a Transaction Database using the FHUQI-Miner Algorithm
- Example 153 : Mining the Top-K Quantitative High Utility Itemsets in a Transaction Database using the TKQ Algorithm
- Example 154 : Mining the Correlated Quantitative High Utility Itemsets in a Transaction Database using the CHUQI-Miner Algorithm
- Example 155 : Mining Irregular High-Utility Itemsets using the PHM_irregular algorithm
- Example 156 : Mining Local High Utility Itemsets in a Transaction Database using the LHUI-Miner Algorithm
- Example 157 : Mining Peak High Utility Itemsets in a Transaction Database using the PHUI-Miner Algorithm
- Example 158 : Mining Locally Trending High Utility Itemsets in a Transaction Database using the LTHUI-Miner Algorithm
- Example 159 : Mining Low-cost High Utility Itemsets using the LCIM algorithm
- Example 160 : Mining High Utility Association Rules using the HGB / HGB-ALL Algorithm
Association Rule Mining
- Example 161 : Mining All Association Rules
- Example 162 : Mining All Association Rules with the lift measure
- Example 163 : Mining All Association Rules using the GCD algorithm
- Example 164 : Mining the IGB basis of Association Rules
- Example 165 : Mining Perfectly Sporadic Association Rules
- Example 166 : Mining Closed Association Rules
- Example 167 : Mining Minimal Non Redundant Association Rules
- Example 168 : Mining Indirect Association Rules with the INDIRECT algorithm
- Example 169 : Hiding Sensitive Association Rules with the FHSAR algorithm.
- Example 170 : Mining the Top-K Association Rules using the TopKRules algorithm
- Example 171 : Mining the Top-K Association Rules using the ETARM algorithm
- Example 172 : Mining the Top-K Association Rules using the FTARM algorithm
- Example 173 : Mining the Top-K Class Association Rules (association rules with a fixed consequent)
- Example 174 : Mining the Top-K Non-Redundant Association Rules
- Example 175 : Mining Negative Association Rules with the NAR-Miner algorithm
- Example 176 : Mining Positive and Negative Association Rules with PNAR-Eclat or PNAR-AprioriTID
Clustering
- Example 177 : Clustering using the K-Means algorithm
- Example 178 : Clustering using the K-Means++ algorithm
- Example 179 : Clustering using the DBScan algorithm
- Example 180 : Clustering using the AEDBScan algorithm
- Example 181 : Clustering using the DPC algorithm
- Example 182 : Using Optics to extract a cluster-ordering of points and DB-Scan style clusters
- Example 183 : Clustering using the Bisecting K-Means algorithm
- Example 184 : Clustering using a Hierarchical Clustering algorithm
- Example 185 : Generate a synthetic clustering dataset
- Example 186 : Visualizing clusters using the Cluster Viewer
- Example 187 : Visualizing instances using the Instance Viewer
Sequential Pattern Mining
- Example 188 : Mining Frequent Sequential Patterns Using the PrefixSpan Algorithm
- Example 189 : Mining Frequent Sequential Patterns Using the GSP Algorithm
- Example 190 : Mining Frequent Sequential Patterns Using the SPADE Algorithm
- Example 191 : Mining Frequent Sequential Patterns Using the CM-SPADE Algorithm
- Example 192 : Mining Frequent Sequential Patterns Using the SPAM Algorithm
- Example 193 : Mining Frequent Sequential Patterns Using the CM-SPAM Algorithm
- Example 194 : Mining Frequent Sequential Patterns Using the FAST Algorithm
- Example 195 : Mining Frequent Sequential Patterns Using the LAPIN Algorithm
- Example 196 : Mining Frequent Closed Sequential Patterns Using the ClaSP Algorithm
- Example 197 : Mining Frequent Closed Sequential Patterns Using the CM-ClaSP Algorithm
- Example 198 : Mining Frequent Closed Sequential Patterns Using the CloFAST Algorithm
- Example 199 : Mining Frequent Closed Sequential Patterns Using the CloSpan Algorithm
- Example 200 : Mining Frequent Closed Sequential Patterns Using the BIDE+ Algorithm
- Example 201 : Mining Frequent Closed Sequential Patterns by Post-Processing using SPAM or PrefixSpan
- Example 202 : Mining Frequent Maximal Sequential Patterns Using the MaxSP Algorithm
- Example 203 : Mining Frequent Maximal Sequential Patterns using the VMSP Algorithm
- Example 204 : Mining Frequent Sequential Generator Patterns Using the FEAT Algorithm
- Example 205 : Mining Frequent Sequential Generator Patterns Using the FSGP Algorithm
- Example 206 : Mining Frequent Sequential Generator Patterns Using the VGEN Algorithm
- Example 207: Mining Nonoverlapping Sequential Patterns in One or Many Sequences Using the NOSEP Algorithm
- Example 208 : Mining Compressing Sequential Patterns Using the GoKrimp Algorithm
- Example 209 : Mining Frequent Top-K Sequential Patterns Using the TKS Algorithm
- Example 210 : Mining Frequent Top-K Sequential Patterns Using the TSP Algorithm
- Example 211 : Mining Frequent Multi-dimensional Sequential Patterns Using SeqDIM (with PrefixSpan and Apriori)
- Example 212 : Mining Frequent Closed Multi-dimensional Sequential Patterns Using SeqDIM/Songram (with Bide+ and AprioriClose)
- Example 213 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
- Example 214 : Mining Sequential Patterns with flexible constraints from a Time-Extended Sequence Database with the SPM-FC-L algorithm
- Example 215 : Mining Sequential Patterns with flexible constraints from a Time-Extended Sequence Database with the SPM-FC-P algorithm
- Example 216 : Mining Closed Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
- Example 217 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database containing Valued Items (source code version only)
- Example 218 : Mining Closed Multi-dimensional Sequential Patterns from a Time-Extended Sequence Database
- Example 219 : Mining Progressive Sequential Patterns using the ProSecCo algorithm
- Example 220 : Finding all occurrences of some sequential pattern(s) by post-processing using the Occur algorithm
- Example 221 : the QCSP algorithm for mining the top-k quantive cohesive sequential patterns in a single sequence or in multiple sequences (thanks to Lens Fereman et al.)
- Example 222: Mining Cost-Efficient Sequential Patterns Using CEPB Algorithm
- Example 223: Mining Cost-Efficient Sequential Patterns Using CorCEPB Algorithm
- Example 224: Mining Cost-Efficient Sequential Patterns Using CEPN Algorithm
- Example 225: Mining Frequent Time Interval Related Patterns Using the FastTIRP Algorithm
- Example 226: Mining Frequent Time Interval Related Patterns Using the VertTIRP Algorithm
- Example 227: Mining Top-K Sequential Patterns with Leverage Using the SKOPUS Algorithm
- Example 228: Mining Compressing Patterns in Genome Sequences using the HMG Algorithm
Sequential Rule Mining
- Example 229 : Mining Sequential Rules Common to Several Sequences with the CMRules algorithm
- Example 230 : Mining Sequential Rules Common to Several Sequences with the CMDeo algorithm
- Example 231 : Mining Sequential Rules Common to Several Sequences with the RuleGrowth algorithm
- Example 232 : Mining Sequential Rules Common to Several Sequences with the ERMiner algorithm
- Example 233 : Mining Sequential Rules between Sequential Patterns with the RuleGen algorithm
- Example 234 : Mining Sequential Rules Common to Several Sequences with the Window Size Constraint using TRuleGrowth
- Example 235 : Mining the Top-K Sequential rules
- Example 236: Mining the Top-K Class Sequential rules (sequential rules with a fixed consequent)
- Example 237 : Mining the Top-K Non-Redundant Sequential rules
Sequence Prediction (source code version only)
- Example 238 : Perform Sequence Prediction using the CPT+ Sequence Prediction Model
- Example 239 : Perform Sequence Prediction using the CPT Sequence Prediction Model
- Example 240 : Perform Sequence Prediction using the PPM Sequence Prediction Model
- Example 241 : Perform Sequence Prediction using the DG Sequence Prediction Model
- Example 242 : Perform Sequence Prediction using the AKOM Sequence Prediction Model
- Example 243 : Perform Sequence Prediction using the TDAG Sequence Prediction Model
- Example 244 : Perform Sequence Prediction using the LZ78 Sequence Prediction Model
- Example 245 : Comparing Several Sequence Prediction Models
Periodic pattern mining
- Example 246 : Mining Periodic Frequent Patterns using the PFPM algorithm
- Example 247 : Mining Periodic Frequent Patterns using the PFTree algorithm
- Example 248 : Mining the Top-K Periodic Frequent Patterns using the MTKPP algorithm
- Example 249 : Mining the Top-K Periodic Frequent Patterns using the TRCT algorithm
- Example 250 : Mining Local Periodic Frequent Patterns using the LPP-Growth, LPPM_breadth or LPPM_depth algorithm
- Example 251 : Mining Stable Periodic Frequent Patterns using the SPP-Growth algorithm
- Example 252 : Mining the Top-k Stable Periodic Frequent Patterns using the TSPIN algorithm
- Example 253 : Mining Self-Reliant Periodic Frequent Patterns using the SRPFPM algorithm
- Example 254 : Mining Productive Periodic Frequent Patterns using the PPFP algorithm
- Example 255 : Mining Non-Redundant Periodic Frequent Patterns using the NPFPM algorithm
- Example 256 : Mining Periodic High-Utility Itemsets using the PHM algorithm
- Example 257 : Mining Periodic High-Utility Itemsets with positive or negative utility values using the PHMN algorithm
- Example 258 : Mining Periodic High-Utility Itemsets with positive or negative utility values using the PHMN+ algorithm
- Example 259 : Mining Periodic Patterns in Multiple Sequences using the MPFPS-BFS or MPFPS-DFS algorithms
- Example 260 : Mining Rare Correlated Periodic Patterns in Multiple Sequences using the MRCPPS algorithm
Episode Mining
- Example 261 : Mining the Top-K Frequent Episodes in a Complex Sequence using the TKE algorithm
- Example 262 : Mining Frequent Episodes in a Complex Sequence using the EMMA algorithm, which counts the support based on the head frequency
- Example 263 : Mining Frequent Episodes in a Complex Sequence using the AFEM algorithm, which counts the support based on the head frequency
- Example 264 : Mining Frequent Episodes in a Complex Sequence using the MINEPI+ algorithm, which counts the support based on the head frequency
- Example 265 : Mining Frequent Episodes in a Complex Sequence using the MINEPI algorithm, which counts the support based on minimal occurrences
- Example 266 : Mining Maximal Frequent Episodes in a Complex Sequence using the MaxFEM algorithm, which counts the support based on the head frequency
- Example 267 : Mining Partially-Ordered Episode Rules in a Complex Sequence using the POERM, or POERM-ALL algorithms
- Example 268 : Mining Partially-Ordered Episode Rules in a Complex Sequence using the POERMH algorithm
- Example 269: Mining Episode Rules in a Complex Sequence using the TKE, MINEPI+, AFEM, or EMMA algorithm
- Example 270: Mining Episode Rules in a Complex Sequence with the Non-Overlapping Frequency using the NONEPI algorithm
- Example 271: Mining Frequent Episodes and Rules in a Complex Sequence using the EMDO or EMDO-Rules algorithms
- Example 272 : Mining High Utility Episodes using the HUE-SPAN algorithm
- Example 273 : Mining High Utility Episodes using the UP-SPAN algorithm
- Example 274 : Mining the Top-K High Utility Episodes using the TUP algorithm
- Example 275: Mining frequent sequential patterns with periodic wilcard gaps in a sequence of characters with the MAPD algorithm
- Example 276: Mining self-adaptive one-off weak-gap strong sequential patterns in a sequence of characters with the OWSP-Miner algorithm
Graph Pattern Mining
- Example 277 : Mining the Top-K Frequent Subgraphs in a Graph Database using the TKG algorithm
- Example 278 : Mining Frequent Subgraphs in a Graph Database using the gSpan algorithm
- Example 279 : Mining Frequent Closed Subgraphs in a Graph Database using the cgSpan algorithm
- Example 280 : Mining Significant Trend Sequence in a Dynamic Attributed Graph using the TSeqMiner algorithm
- Example 281 :Mining Frequent Subgraphs using the AERMiner Algorithm
Text Mining
- Example 283 : Clustering Texts with a text clusterer
- Example 284 : Classifying Text documents using a Naive Bayes approach (source code version only)
Time Series Mining
- Example 285 : Vizualize time series using the time series viewer
- Example 286 : Calculate the prior moving average of time series
- Example 287 : Calculate the cumulative moving average of time series
- Example 288 : Calculate the central moving average of time series
- Example 289 : Calculate the min max normalization of a time series
- Example 290 : Calculate the standardization of a time series
- Example 291 : Calculate the median smoothing of a time series
- Example 292 : Calculate the exponential smoothing of a time series
- Example 293 : Calculate the first order differencing of a time series
- Example 294 : Calculate the second order differencing of a time series
- Example 295 : Calculate the piecewise aggregate approximation of time series
- Example 296 : Calculate the autocorelation function of a time series
- Example 297 : Calculate the regression line of a time series using the least square method, and perform time series forecasting
- Example 298 : Split time series by length
- Example 299 : Split time series by number of segments
- Example
300 : Convert time series to sequences using the SAX
algorithm (useful to be able to apply sequential pattern mining/rule
algorithms to time series)
Besides the above example for time series mining, clustering algorithms such as K-Means can also be applied to time-series.
Classification
- Example 301 : How to train the ID3 classifier to perform classification (source code version only)
- Example 302 : How to train the KNN classifier to perform classification (source code version only)
- Example 303 : How to train the CMAR classifier to perform classification (source code version only)
- Example 304 : How to train the ACAC classifier to perform classification (source code version only)
- Example 305 : How to train the ACCF classifier to perform classification (source code version only)
- Example 306 : How to train the ACN classifier to perform classification (source code version only)
- Example 307 : How to train the ADT classifier to perform classification (source code version only)
- Example 308 : How to train the CBA classifier to perform classification (source code version only)
- Example 309 : How to train the CBA2 classifier to perform classification (source code version only)
- Example 310 : How to train the CMAR classifier to perform classification (source code version only)
- Example 311 : How to train the L3 classifier to perform classification (source code version only)
- Example 312 : How to train the CMAR classifier to perform classification (source code version only)
- Example 313 : How to train the MAC classifier to perform classification (source code version only)
- Example 314 : Run an experiment to compare many classifiers such as ID3, CMAR, ACCF, CBA and CBA2. (source code version only)
- Example 315 : Transform a CSV dataset into a Transaction Database with Utility Information for Classification using the UBTGen Algorithm
- Example 316 : Mining Top-k High Utility Itemsets based on the Information Gain for Supervised Learning using the THUIsl Algorithm
Dataset transformation tools
- Example 317 : Converting a sequence database to SPMF format (CSV, KOSARAK, IBM, BMS, Snake...)
- Example 318 : Converting a transaction database to SPMF format (CSV...)
- Example 319 : Converting a sequence database to a transaction database
- Example 320 : Converting a transaction database to a sequence database
- Example 321 : Converting a sequence database with cost values to a transaction database with cost values
- Example 322 : Generating a synthetic sequence database
- Example 323 : Generating a synthetic sequence database with timestamps
- Example 324 : Generating a synthetic transaction database
- Example 325 : Generating synthetic utility values for a transaction database without utility values
- Example 326 : Generating a a file or random item weights for a transaction database
- Example 327 : Add consecutive timestamps to a sequence database without timestamps
- Example 328 : Using the ARFF format in the source code version of SPMF
- Example 329 : Using a TEXT file as input in the source code version of SPMF
- Example 330 : Fix a transaction database
- Example 331 : Fix a sequence database
- Example 332 : Fix item ids in a transaction database
- Example 333 : Fix item ids in a transaction database with utility information
- Example 334 : Fix a transaction database with utility and time information
- Example 335 : Remove utility information from a transaction database
- Example 336 : Resize a database in SPMF format (a text file)
- Example 337 : Sample records from a database in SPMF format (a text file)
- Example 338 : Remove duplicate records from a database in SPMF format (a text file)
Dataset statistics tools
- Example 339 : Calculating statistics for a sequence database
- Example 340 : Calculating statistics for a transaction database
- Example 341 : Calculating statistics for a transaction database with utility information
- Example 342 : Calculating statistics for a graph database
- Example 343 : Calculating statistics for a product transaction database
- Example 344 : Calculating statistics for a sequence database with cost and binary utility
- Example 345 : Calculating statistics for a sequence database with cost and numeric utility
- Example 346 : Calculating statistics for a sequence database with utility
- Example 347 : Calculating statistics for a time-extended sequence database
- Example 348 : Calculating statistics for an uncertain transaction database
- Example 349 : Calculating statistics for a transaction database with cost and utility
- Example 350 : Calculating statistics for a transaction database with utility and period information
- Example 351 : Calculating statistics for a transaction database with utility and timestamps
- Example 352 : Calculating statistics for an event sequence
- Example 353 : Calculating statistics for an interval sequence database
- Example 354 : Calculating statistics for a multi-dimensional sequence database
- Example 355 : Calculating statistics for a multi-dimensional sequence database with timestamps
- Example 356 : Calculating statistics for a file with double vectors (instances) for clustering
- Example 357 : Calculating statistics for time series
Dataset viewer tools
- Example 358 : View the content of an ARFF file using the ARFF Viewer
- Example 359 : View the content of an CSV file using the CSV Viewer
- Example 360 : View the content of an FASTA file using the FASTA Viewer
- Example 361 : View a graph database file with the Graph Viewer
- Example 362 : View an event sequence with the Event Sequence Viewer
- Example 363 : View a sequence database file with the Sequence Database Viewer
- Example 364 : View a sequence database cost binary utility file with the Sequence Database Cost Binary Utility Viewer
- Example 365 : View a sequence database cost numeric utility file with the Sequence Database Cost Numeric Utility Viewer
- Example 366 : View a time-extended sequence database with the Time-Extended Sequence Database Viewer
- Example 367 : View an MD sequence database with the MD Sequence Database Viewer
- Example 368 : View an MD time sequence database with the MD Time Sequence Database Viewer
- Example 369 : View a sequence utility database file with the Sequence Utility Database Viewer
- Example 370 : View a transaction database file with the Transaction Database Viewer
- Example 371 : View an uncertain transaction database file with the Uncertain Transaction Database Viewer
- Example 372 : View a utility transaction database file with the Utility Transaction Database Viewer
- Example 373 : View a utility time transaction database file with the Utility Time Transaction Database Viewer
- Example 374 : View a utility period transaction database file with the Utility Period Transaction Database Viewer
- Example 375 : View a product transaction database file with the Profit Transaction Database Viewer
- Example 376 : View a cost utility transaction database file with the Cost Utility Transaction Database Viewer
- Example 377 : View a Time-Interval Sequence Database with the Time Interval Sequence Database Viewer
- Example 378 : View a taxonomy file with the Taxonomy Viewer
Pattern viewer tools
- Example379 : View itemsets using the Itemsets-Item Matrix Viewer
- Example 380 : Visualize association rules
- Example 381 : Visualize closed association rules
- Example 382 : Visualize association rules with lift
- Example 383 : Visualize association rules with lift and multiple support thresholds
- Example 384 : Visualize Top-K Association rules
- Example 385 : Visualize Top-K Class Association rules
- Example 386 : Visualize Sporadic Association rules
- Example 387 : Visualize Top-K NonRedundant Association rules
- Example 388 : Visualize Top-K Minimal NonRedundant Association rules
- Example 389 : Visualize High-utility Association rules
- Example 390 : Visualize Frequent itemsets
- Example 391 : Visualize Frequent generator itemsets
- Example 392 : Visualize High Utility itemsets
- Example 393 : Visualize Closed High Utility itemsets
- Example 394 : Visualize Correlated High Utility itemsets
- Example 395 : Visualize Frequent High Utility itemsets
- Example 396 : Visualize Frequent closed itemsets
- Example 397 : Visualize Frequent maximal itemsets
- Example 398 : Visualize Frequent minimal rare itemsets
- Example 399 : Visualize Perfectly rare itemsets
- Example 400 : Visualize Maximal High Utility itemsets
- Example 401 : Visualize Minimal High Utility itemsets
- Example 402 : Visualize Top-K High Utility itemsets
- Example 403 : Visualize High Average Utility itemsets
- Example 404 : Visualize High Utility Generators
- Example 405 : Visualize Generator High Utility itemsets
- Example 406 : Visualize Generators of High Utility itemsets
- Example 407 : Visualize Erasable itemsets
- Example 408 : Visualize Rare correlated itemsets
- Example 409 : Visualize Uncertain frequent itemsets
- Example 410 : Visualize Self sufficient itemsets
- Example 411 : Visualize Frequent itemsets with multi thresholds
- Example 412 : Visualize Frequent fuzzy itemsets
- Example 413 : Visualize Frequent weighted itemsets
- Example 414 : Visualize Multi frequent fuzzy itemsets
- Example 415 : Visualize Periodic frequent itemsets
- Example 416 : Visualize Stable periodic itemsets
- Example 417 : Visualize Top-K Stable periodic itemsets
- Example 418 : Visualize Periodic High Utility itemsets
- Example 419 : Visualize Productive periodic frequent itemsets
- Example 420 : Visualize Rare correlated itemsets MultiSequences
- Example 421 : Visualize Local periodic frequent itemsets
- Example 422 : Visualize Peak high Utility itemsets
- Example 423 : Visualize Local high Utility itemsets
- Example 424 : Visualize locally trending HUIs
- Example 425 : Visualize Skyline frequent HUIs
- Example 426 : Visualize Skyline HUIs
- Example 427 : Visualize Onshelf High Utility itemsets
- Example 428 : Visualize Crosslevel High Utility itemsets
- Example 429 : Visualize Multilevel High Utility itemsets
- Example 430 : Visualize Nonredundant Periodic itemsets
- Example 431 : Visualize Quantitative high Utility itemsets
- Example 432 : Visualize High Utility itemsets with length constraint
- Example 433 : Visualize Frequent itemsets and generators
- Example 434 : Visualize Periodic Frequent itemsets from multiple sequences
- Example 435 : Visualize Sequential rules
- Example 436 : Visualize Top-K Frequent Sequential rules
- Example 437 : Visualize Top-K non redundant sequential rules
- Example 438 : Visualize High utility sequential rules
- Example 439 : Visualize Sequential rules with strings
- Example 440 : Visualize frequent episodes
- Example 441 : Visualize top-k frequent episodes
- Example 442 : Visualize high utility episodes
- Example 443 : Visualize top-k high utility episodes
- Example 444 : Visualize Episode rules
- Example 445 : Visualize Frequent episode rules
- Example 446 : Visualize frequent sequential patterns
- Example 447 : Visualize top-k frequent sequential patterns
- Example 448 : Visualize closed frequent sequential patterns
- Example 449 : Visualize maximal frequent sequential patterns
- Example 450 : Visualize generator frequent sequential patterns
- Example 451 : Visualize high utility sequential patterns
- Example 452 : Visualize cost efficient sequential patterns
- Example 453 : Visualize compressing sequential patterns
- Example 454 : Visualize frequent sequential patterns with occurrences
- Example 455 : Visualize frequent sequential patterns with timestamps
- Example 456 : Visualize closed frequent sequential patterns with timestamps
- Example 457 : Visualize high utility probability sequential patterns
- Example 458 : Visualize progressive frequent sequential patterns
- Example 459 : Visualize top-k frequent sequential patterns with leverage
- Example 460 : Visualize self adaptive one off weakgap strong sequential pattern
- Example 461 : Visualize sequential patterns with wildcard gaps
- Example 462 : Visualize frequent subgraphs
Other GUI tools
- Example 463 : Open the Algorithm Explorer to view informations about algorithms
- Example 464 : Create a workflow using the SPMF Workflow editor
- Example 465 : Monitor the JVM's Memory using the Memory Viewer
- Example 466 : Visualize patterns in a text file with the Pattern Viewer
- Example 467 : Open a text file using the SPMF text editor
- Example 468 : Open a text file with the System Text Editor
- Example 469 : Open the SPMF developers tools
- Example 470 : Download an offline copy of the SPMF documentation on your computer
- Example 471 : Visualize algorithms from SPMF as a graph using the Algorithm Graph Visualizer
- Example 472 : Compare two pattern files with the SPMF Pattern Diff Analyzer
Other tools
Experiments
- Example 474 : Run experiments to compare the performance of one or more algorithms on a dataset (one parameter is varied)
- Example 475 : Run experiments to compare the scalability of one or more algorithms on a dataset (dataset size is varied, input parameters are fixed)
Copyright © 2008-2026 Philippe Fournier-Viger. All rights reserved.
