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 Closed Itemsets Using the AprioriClose Algorithm
- Example 12 : Mining Frequent Closed Itemsets Using the DCI_Closed Algorithm
- Example 13 : Mining Frequent Closed Itemsets Using the Charm / dCharm Algorithm
- Example 14 : Mining Frequent Closed Itemsets Using the LCM Algorithm
- Example 15 : Mining Frequent Closed Itemsets Using the FPClose Algorithm
- Example 16 : Mining Frequent Closed Itemsets Using the NAFCP Algorithm
- Example 17 : Mining Frequent Closed Itemsets Using the NEclatClosed Algorithm
- Example 18 : Mining Frequent Maximal Itemsets Using the FPMax Algorithm
- Example 19 : Mining Frequent Maximal Itemsets Using the Charm-MFI Algorithm
- Example 20 : Mining Frequent Generator Itemsets Using the DefMe Algorithm
- Example 21 : Mining Frequent Itemsets and Identify the Generators Using the Pascal Algorithm
- Example 22 : Mining Frequent Closed Itemsets and Minimal Generators Using the Zart Algorithm
- Example 23 : Mining Minimal Rare Itemsets Using the AprioriRare Algorithm
- Example 24 : Mining Perfectly Rare Itemsets Using the AprioriInverse Algorithm
- Example 25 : Mining Rare Correlated Itemsets Using the CORI Algorithm
- Example 26 : Mining Rare Itemsets Using the RP-Growth Algorithm
- Example 27 : Mining Closed Itemsets from a Data Stream Using the CloStream Algorithm (source code version only)
- Example 28 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec Algorithm (source code version only)
- Example 29 : Mining Recent Frequent Itemsets from a Data Stream Using the estDec+ Algorithm (source code version only)
- Example 30 : Mining Frequent Itemsets from Uncertain Data with the UApriori Algorithm
- Example 31 : Mining Erasable Itemsets from a Product Database with the VME algorithm
- Example 32 : Building, updating incrementally and using an Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
- Example 33 : Building, updating incrementally and using a Memory-Efficient Itemset-Tree to generate targeted frequent itemsets and association rules (source code version only)
- Example 34 : Mining Frequent Itemsets with Multiple Support Thresholds Using the MSApriori Algorithm
- Example 35 : Mining Frequent Itemsets with Multiple Support Thresholds Using the CFPGrowth++ Algorithm
- Example 36 : Mining Fuzzy Frequent Itemsets in a quantitative transaction database using the FFI-Miner algorithm
- Example 37 : Mining Multiple Fuzzy Frequent Itemsets in a quantitatve transaction database using the MFFI-Miner algorithm
- Example 38 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the LevelWise algorithm
- Example 39 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the DFI-Growth algorithm
- Example 40 : Deriving Frequent Itemsets from Frequent Closed Itemsets using the DFI-List algorithm
- Example 41 : Mining Self-Sufficient Itemsets using the Opus-Miner algorithm
- Example 42 : Mining the Top-K Frequent Itemsets by Using the Apriori(top-k) Algorithm
- Example 43 : Mining the Top-K Frequent Itemsets by Using the FP-Growth(top-k) Algorithm
- Example 44 : Mining Compressing Itemsets using the Krimp Algorithm
- Example 45 : Mining Compressing Itemsets using the SLIM Algorithm
High-Utility Pattern Mining
- Example 46 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the Two-Phase Algorithm
- Example 47 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the FHM Algorithm
- Example 48 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the EFIM Algorithm
- Example 49 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUI-Miner Algorithm
- Example 50 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUP-Miner Algorithm
- Example 51 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UP-Growth / UP-Growth+ Algorithm
- Example 52 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the IHUP Algorithm
- Example 53 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the mHUIMiner Algorithm
- Example 54 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HMiner Algorithm
- Example 55 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the HUIM-SU Algorithm
- Example 56 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the ULB-Miner Algorithm
- Example 57 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UFH Algorithm
- Example 58 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the d2HUP Algorithm
- Example 59 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the FHIM Algorithm
- Example 60 : Mining High-Utility Itemsets from a Transaction Database with Utility Information using the UPHist Algorithm
- Example 61 : Mining High-Utility Itemsets from a Transaction Database with Utility Information while considering Length Constraints, using the FHM+ algorithm
- Example 62 : Mining Cross-Level High-Utility Itemsets from a Transaction Database with Utility Information using the CLH-Miner algorithm
- Example 63: Mining Cross-Level High-Utility Itemsets from a Transaction Database with Utility Information using the FEACP algorithm
- Example 64 : Mining the Multi-Level High-Utility Itemsets from a Transaction Database with Utility Information using the MLHUI-Miner algorithm
- Example 65 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the FCHM_bond algorithm
- Example 66 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the FCHM_allconfidence algorithm
- Example 67 : Mining Correlated High-Utility Itemsets in a Transaction Database with Utility Information using the ECHUM algorithm
- Example 68 : Mining Frequent High-Utility Itemsets from a Transaction Database with Utility Information using the FHMFreq Algorithm
- Example 69 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the FHN Algorithm
- Example 70 : Mining High-Utility Itemsets from a Transaction Database with Positive or Negative Unit Profit using the HUINIV-Mine Algorithm
- Example 71 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the FOSHU Algorithm
- Example 72 : Mining On-Shelf High-Utility Itemsets from a Transaction Database using the TS-HOUN Algorithm
- Example 73 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the EIHI Algorithm (source code version only)
- Example 74 : Incremental High-Utility Itemset Mining in a Transaction Database with utility information using the HUI-LIST-INS Algorithm (source code version only)
- Example 75 : Incremental Closed High-Utility Itemset Mining in a Transaction Database with utility information using the INCCHUI Algorithm (source code version only)
- Example 76 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the EFIM-Closed Algorithm
- Example 77 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUI-Miner Algorithm
- Example 78 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUI-Miner(Max) Algorithm
- Example 79 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CHUD Algorithm
- Example 80 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the CLS-Miner Algorithm
- Example 81 : Mining Closed High-Utility Itemsets from a transaction database with utility information using the HMiner-Closed Algorithm
- Example 82 : Mining Generators of High-Utility Itemsets from a transaction database with utility information using the GHUI-Miner Algorithm
- Example 83 : Mining High-Utility Generator Itemsets from a transaction database with utility information using the HUG-Miner Algorithm
- Example 84: Mining Closed High Utility Itemsets and High-Utility Generator Itemsets from a transaction database with utility information using the HUCI_Miner Algorithm
- Example 85 : Mining Minimal High-Utility Itemsets from a transaction database with utility information using the MinFHM Algorithm
- Example 86 : Mining Skyline High-Utility Itemsets in a transaction database with utility information using the SkyMine Algorithm
- Example 87 : Mining Skyline Frequent High-Utility Itemsets in a transaction database with utility information using the SFUI_UF Algorithm
- Example 88 : Mining Skyline Frequent High-Utility Itemsets in a transaction database with utility information using the SFU-CE Algorithm
- Example 89 : Mining High-Utility Sequential Rules from a Sequence Database with utility information using the HUSRM Algorithm
- Example 90 : Mining High-Utility Sequential Patterns from a Sequence Database with utility information using the USPAN Algorithm
- Example 91 : Mining High-Utility Probability Sequential Patterns from a Sequence Database with utility and probability information using the PHUSPM Algorithm
- Example 92 : Mining High-Utility Probability Sequential Patterns from a Sequence Database with utility information and probability using the UHUSPM Algorithm
- Example 93 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO algorithm
- Example 94 : Mining High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA algorithm
- Example 95 : Mining High Utility Itemsets Using Ant Colony Optimization Algorithm with the HUIM-ACO algorithm
- Example 96 : Mining High Utility Itemsets Using Hill-Climbing with the HUIM-HC algorithm
- Example 97 : Mining High Utility Itemsets Using Simulated Annealing with the HUIM-SA algorithm
- Example 98 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-BPSO-tree algorithm
- Example 99 : Discovery of High Utility Itemsets Using a Genetic Algorithm with the HUIM-GA-tree algorithm
- Example 100 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIF-PSO algorithm
- Example 101 : Mining High-Utility Itemsets based on Particle Swarm Optimization with the HUIM-SPSO algorithm
- Example 102 : Mining High-Utility Itemsets Using a Genetic Algorithm with the HUIF-GA algorithm
- Example 103 : Mining High-Utility Itemsets Using a Bat Algorithm with the HUIF-BA algorithm
- Example 104 : Mining High-Utility Itemsets Using Artificial Fish Swarm Algorithm with the HUIM-AF algorithm
- Example 105 : Mining High-Utility Itemsets Using a Artificial Bee Colony Algorithm with the HUIF-ABC algorithm
- Example 106 : Mining Skyline Frequent-Utility Patterns using the SFUPMinerUemax algorithm
- Example 107 : Mining Skyline Frequent-Utility Patterns using the EMSFUI_B algorithm
- Example 108 : Mining Skyline Frequent-Utility Patterns using the EMSFUI_D algorithm
- Example 109 : Mining the Top-k high-utility itemsets using the TKU algorithm
- Example 110 : Mining the Top-k high-utility itemsets using the TKO (basic) algorithm
- Example 111 : Mining the Top-k high-utility itemsets using the THUI algorithm
- Example 112 : Mining the Top-k high-utility itemsets using the TKU-CE algorithm
- Example 113 : Mining the Top-k high-utility itemsets using the TKU-CE+ algorithm
- Example 114 : Mining the Top-k high-utility itemsets in a data stream using the FHMDS algorithm
- Example 115 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the HAUI-Miner Algorithm
- Example 116 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the EHAUPM Algorithm
- Example 117 : Mining High Average-Utility Itemsets in a Transaction Database with Utility Information using the HAUIM-GMU Algorithm
- Example 118 : Mining High Average-Utility Itemsets with Multiple Thresholds in a Transaction Database using the HAUI-MMAU Algorithm
- Example 119 : Mining High Average-Utility Itemsets with Multiple Thresholds in a Transaction Database using the MEMU Algorithm
- Example 120 : Mining the Top-K High Average-Utility Itemsets in a Transaction Database using the ETAUIM Algorithm
- Example 121 : Mining Quantitative High Utility Itemsets in a Transaction Database using the VHUQI Algorithm
- Example 122 : Mining Quantitative High Utility Itemsets in a Transaction Database using the FHUQI-Miner Algorithm
- Example 123 : Mining the Top-K Quantitative High Utility Itemsets in a Transaction Database using the TKQ Algorithm
- Example 124 : Mining the Correlated Quantitative High Utility Itemsets in a Transaction Database using the CHUQI-Miner Algorithm
- Example 125 : Mining Irregular High-Utility Itemsets using the PHM_irregular algorithm
- Example 126 : Mining Local High Utility Itemsets in a Transaction Database using the LHUI-Miner Algorithm
- Example 127 : Mining Peak High Utility Itemsets in a Transaction Database using the PHUI-Miner Algorithm
- Example 128 : Mining Locally Trending High Utility Itemsets in a Transaction Database using the LTHUI-Miner Algorithm
- Example 129 : Mining Low-cost High Utility Itemsets using the LCIM algorithm
- Example 130 : Mining High Utility Association Rules using the HGB / HGB-ALL Algorithm
Association Rule Mining
- Example 131 : Mining All Association Rules
- Example 132 : Mining All Association Rules with the lift measure
- Example 133 : Mining All Association Rules using the GCD algorithm
- Example 134 : Mining the IGB basis of Association Rules
- Example 135 : Mining Perfectly Sporadic Association Rules
- Example 136 : Mining Closed Association Rules
- Example 137 : Mining Minimal Non Redundant Association Rules
- Example 138 : Mining Indirect Association Rules with the INDIRECT algorithm
- Example 139 : Hiding Sensitive Association Rules with the FHSAR algorithm.
- Example 140 : Mining the Top-K Association Rules
- Example 141 : Mining the Top-K Class Association Rules (association rules with a fixed consequent)
- Example 142 : Mining the Top-K Non-Redundant Association Rules
Clustering
- Example 143 : Clustering using the K-Means algorithm
- Example 144 : Clustering using the K-Means++ algorithm
- Example 145 : Clustering using the DBScan algorithm
- Example 146 : Using Optics to extract a cluster-ordering of points and DB-Scan style clusters
- Example 147 : Clustering using the Bisecting K-Means algorithm
- Example 148 : Clustering using a Hierarchical Clustering algorithm
- Example 149 : Generate a synthetic clustering dataset
- Example 150 : Visualizing clusters using the Cluster Viewer
- Example 151 : Visualizing instances using the Instance Viewer
Sequential Pattern Mining
- Example 152 : Mining Frequent Sequential Patterns Using the PrefixSpan Algorithm
- Example 153 : Mining Frequent Sequential Patterns Using the GSP Algorithm
- Example 154 : Mining Frequent Sequential Patterns Using the SPADE Algorithm
- Example 155 : Mining Frequent Sequential Patterns Using the CM-SPADE Algorithm
- Example 156 : Mining Frequent Sequential Patterns Using the SPAM Algorithm
- Example 157 : Mining Frequent Sequential Patterns Using the CM-SPAM Algorithm
- Example 158 : Mining Frequent Sequential Patterns Using the FAST Algorithm
- Example 159 : Mining Frequent Sequential Patterns Using the LAPIN Algorithm
- Example 160 : Mining Frequent Closed Sequential Patterns Using the ClaSP Algorithm
- Example 161 : Mining Frequent Closed Sequential Patterns Using the CM-ClaSP Algorithm
- Example 162 : Mining Frequent Closed Sequential Patterns Using the CloFAST Algorithm
- Example 163 : Mining Frequent Closed Sequential Patterns Using the CloSpan Algorithm
- Example 164 : Mining Frequent Closed Sequential Patterns Using the BIDE+ Algorithm
- Example 165 : Mining Frequent Closed Sequential Patterns by Post-Processing using SPAM or PrefixSpan
- Example 166 : Mining Frequent Maximal Sequential Patterns Using the MaxSP Algorithm
- Example 167 : Mining Frequent Maximal Sequential Patterns using the VMSP Algorithm
- Example 168 : Mining Frequent Sequential Generator Patterns Using the FEAT Algorithm
- Example 169 : Mining Frequent Sequential Generator Patterns Using the FSGP Algorithm
- Example 170 : Mining Frequent Sequential Generator Patterns Using the VGEN Algorithm
- Example 171: Mining Nonoverlapping Sequential Patterns in One or Many Sequences Using the NOSEP Algorithm
- Example 172 : Mining Compressing Sequential Patterns Using the GoKrimp Algorithm
- Example 173 : Mining Frequent Top-K Sequential Patterns Using the TKS Algorithm
- Example 174 : Mining Frequent Top-K Sequential Patterns Using the TSP Algorithm
- Example 175 : Mining Frequent Multi-dimensional Sequential Patterns Using SeqDIM (with PrefixSpan and Apriori)
- Example 176 : Mining Frequent Closed Multi-dimensional Sequential Patterns Using SeqDIM/Songram (with Bide+ and AprioriClose)
- Example 177 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
- Example 178 : Mining Sequential Patterns with flexible constraints from a Time-Extended Sequence Database with the SPM-FC-L algorithm
- Example 179 : Mining Sequential Patterns with flexible constraints from a Time-Extended Sequence Database with the SPM-FC-P algorithm
- Example 180 : Mining Closed Sequential Patterns with Time Constraints from a Time-Extended Sequence Database
- Example 181 : Mining Sequential Patterns with Time Constraints from a Time-Extended Sequence Database containing Valued Items (source code version only)
- Example 182 : Mining Closed Multi-dimensional Sequential Patterns from a Time-Extended Sequence Database
- Example 183 : Mining Progressive Sequential Patterns using the ProSecCo algorithm
- Example 184 : Finding all occurrences of some sequential pattern(s) by post-processing using the Occur algorithm
- Example 185 : 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 186: Mining Cost-Efficient Sequential Patterns Using CEPB Algorithm
- Example 187: Mining Cost-Efficient Sequential Patterns Using CorCEPB Algorithm
- Example 188: Mining Cost-Efficient Sequential Patterns Using CEPN Algorithm
- Example 189: Mining Frequent Time Interval Related Patterns Using the FastTIRP Algorithm
- Example 190: Mining Frequent Time Interval Related Patterns Using the VertTIRP Algorithm
Sequential Rule Mining
- Example 191 : Mining Sequential Rules Common to Several Sequences with the CMRules algorithm
- Example 192 : Mining Sequential Rules Common to Several Sequences with the CMDeo algorithm
- Example 193 : Mining Sequential Rules Common to Several Sequences with the RuleGrowth algorithm
- Example 194 : Mining Sequential Rules Common to Several Sequences with the ERMiner algorithm
- Example 195 : Mining Sequential Rules between Sequential Patterns with the RuleGen algorithm
- Example 196 : Mining Sequential Rules Common to Several Sequences with the Window Size Constraint using TRuleGrowth
- Example 197 : Mining the Top-K Sequential rules
- Example 198: Mining the Top-K Class Sequential rules (sequential rules with a fixed consequent)
- Example 199 : Mining the Top-K Non-Redundant Sequential rules
Sequence Prediction (source code version only)
- Example 200 : Perform Sequence Prediction using the CPT+ Sequence Prediction Model
- Example 201 : Perform Sequence Prediction using the CPT Sequence Prediction Model
- Example 202 : Perform Sequence Prediction using the PPM Sequence Prediction Model
- Example 203 : Perform Sequence Prediction using the DG Sequence Prediction Model
- Example 204 : Perform Sequence Prediction using the AKOM Sequence Prediction Model
- Example 205 : Perform Sequence Prediction using the TDAG Sequence Prediction Model
- Example 206 : Perform Sequence Prediction using the LZ78 Sequence Prediction Model
- Example 207 : Comparing Several Sequence Prediction Models
Periodic pattern mining
- Example 208 : Mining Periodic Frequent Patterns using the PFPM algorithm
- Example 209 : Mining Local Periodic Frequent Patterns using the LPP-Growth, LPPM_breadth or LPPM_depth algorithm
- Example 210 : Mining Stable Periodic Frequent Patterns using the SPP-Growth algorithm
- Example 211 : Mining the Top-k Stable Periodic Frequent Patterns using the TSPIN algorithm
- Example 212 : Mining Self-Reliant Periodic Frequent Patterns using the SRPFPM algorithm
- Example 213 : Mining Productive Periodic Frequent Patterns using the PPFP algorithm
- Example 214 : Mining Non-Redundant Periodic Frequent Patterns using the NPFPM algorithm
- Example 215 : Mining Periodic High-Utility Itemsets using the PHM algorithm
- Example 216 : Mining Periodic High-Utility Itemsets with positive or negative utility values using the PHMN algorithm
- Example 217 : Mining Periodic High-Utility Itemsets with positive or negative utility values using the PHMN+ algorithm
- Example 218 : Mining Periodic Patterns in Multiple Sequences using the MPFPS-BFS or MPFPS-DFS algorithms
- Example 219 : Mining Rare Correlated Periodic Patterns in Multiple Sequences using the MRCPPS algorithm
Episode Mining
- Example 220 : Mining the Top-K Frequent Episodes in a Complex Sequence using the TKE algorithm
- Example 221 : Mining Frequent Episodes in a Complex Sequence using the EMMA algorithm, which counts the support based on the head frequency
- Example 222 : Mining Frequent Episodes in a Complex Sequence using the AFEM algorithm, which counts the support based on the head frequency
- Example 223 : Mining Frequent Episodes in a Complex Sequence using the MINEPI+ algorithm, which counts the support based on the head frequency
- Example 224 : Mining Frequent Episodes in a Complex Sequence using the MINEPI algorithm, which counts the support based on minimal occurrences
- Example 225 : Mining Maximal Frequent Episodes in a Complex Sequence using the MaxFEM algorithm, which counts the support based on the head frequency
- Example 226 : Mining Partially-Ordered Episode Rules in a Complex Sequence using the POERM, or POERM-ALL algorithms
- Example 227 : Mining Partially-Ordered Episode Rules in a Complex Sequence using the POERMH algorithm
- Example 228: Mining Episode Rules in a Complex Sequence using the TKE, MINEPI+, AFEM, or EMMA algorithm
- Example 229: Mining Episode Rules in a Complex Sequence with the Non-Overlapping Frequency using the NONEPI algorithm
- Example 230 : Mining High Utility Episodes using the HUE-SPAN algorithm
- Example 231 : Mining High Utility Episodes using the UP-SPAN algorithm
- Example 232 : Mining the Top-K High Utility Episodes using the TUP algorithm
- Example 233: Mining frequent sequential patterns with periodic wilcard gaps in a sequence of characters with the MAPD algorithm
- Example 234: Mining self-adaptive one-off weak-gap strong sequential patterns in a sequence of characters with the OWSP-Miner algorithm
Graph Pattern Mining
- Example 235 : Mining the Top-K Frequent Subgraphs in a Graph Database using the TKG algorithm
- Example 236 : Mining Frequent Subgraphs in a Graph Database using the gSpan algorithm
- Example 237 : Mining Frequent Closed Subgraphs in a Graph Database using the cgSpan algorithm
- Example 238 : Mining Significant Trend Sequence in a Dynamic Attributed Graph using the TSeqMiner algorithm
Text Mining
- Example 240 : Clustering Texts with a text clusterer
- Example 241 : Classifying Text documents using a Naive Bayes approach (source code version only)
Time Series Mining
- Example 242 : Vizualize time series using the time series viewer
- Example 243 : Calculate the prior moving average of time series
- Example 244 : Calculate the cumulative moving average of time series
- Example 245 : Calculate the central moving average of time series
- Example 246 : Calculate the min max normalization of a time series
- Example 247 : Calculate the standardization of a time series
- Example 248 : Calculate the median smoothing of a time series
- Example 249 : Calculate the exponential smoothing of a time series
- Example 250 : Calculate the first order differencing of a time series
- Example 251 : Calculate the second order differencing of a time series
- Example 252 : Calculate the piecewise aggregate approximation of time series
- Example 253 : Calculate the autocorelation function of a time series
- Example 254 : Calculate the regression line of a time series using the least square method, and perform time series forecasting
- Example 255 : Split time series by length
- Example 256 : Split time series by number of segments
- Example
257 : 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 258 : How to train the ID3 classifier to perform classification (source code version only)
- Example 259 : How to train the KNN classifier to perform classification (source code version only)
- Example 260 : How to train the CMAR classifier to perform classification (source code version only)
- Example 261 : How to train the ACAC classifier to perform classification (source code version only)
- Example 262 : How to train the ACCF classifier to perform classification (source code version only)
- Example 263 : How to train the ACN classifier to perform classification (source code version only)
- Example 264 : How to train the ADT classifier to perform classification (source code version only)
- Example 265 : How to train the CBA classifier to perform classification (source code version only)
- Example 266 : How to train the CBA2 classifier to perform classification (source code version only)
- Example 267 : How to train the CMAR classifier to perform classification (source code version only)
- Example 268 : How to train the L3 classifier to perform classification (source code version only)
- Example 269 : How to train the CMAR classifier to perform classification (source code version only)
- Example 270 : How to train the MAC classifier to perform classification (source code version only)
- Example 271 : Run an experiment to compare many classifiers such as ID3, CMAR, ACCF, CBA and CBA2. (source code version only)
Dataset transformation tools
- Example 272 : Converting a sequence database to SPMF format (CSV, KOSARAK, IBM, BMS, Snake...)
- Example 273 : Converting a transaction database to SPMF format (CSV...)
- Example 274 : Converting a sequence database to a transaction database
- Example 275 : Converting a transaction database to a sequence database
- Example 276 : Converting a sequence database with cost values to a transaction database with cost values
- Example 277 : Generating a synthetic sequence database
- Example 278 : Generating a synthetic sequence database with timestamps
- Example 279 : Generating a synthetic transaction database
- Example 280 : Generating synthetic utility values for a transaction database without utility values
- Example 281 : Add consecutive timestamps to a sequence database without timestamps
- Example 282 : Using the ARFF format in the source code version of SPMF
- Example 283 : Using a TEXT file as input in the source code version of SPMF
- Example 284 : Fix a transaction database
- Example 285 : Fix a sequence database
- Example 286 : Fix item ids in a transaction database
- Example 287 : Fix item ids in a transaction database with utility information
- Example 288 : Fix a transaction database with utility and time information
- Example 289 : Remove utility information from a transaction database
- Example 290 : Resize a database in SPMF format (a text file)
Dataset statistics tools
- Example 291 : Calculating statistics for a sequence database
- Example 292 : Calculating statistics for a transaction database
- Example 293 : Calculating statistics for a transaction database with utility information
- Example 294 : Calculating statistics for a graph database
- Example 295 : Calculating statistics for a product transaction database
- Example 296 : Calculating statistics for a sequence database with cost and binary utility
- Example 297 : Calculating statistics for a sequence database with cost and numeric utility
- Example 298 : Calculating statistics for a sequence database with utility
- Example 299 : Calculating statistics for a time-extended sequence database
- Example 300 : Calculating statistics for an uncertain transaction database
- Example 301 : Calculating statistics for a transaction database with cost and utility
- Example 302 : Calculating statistics for a transaction database with utility and period information
- Example 303 : Calculating statistics for a transaction database with utility and timestamps
- Example 304 : Calculating statistics for an event sequence
- Example 305 : Calculating statistics for an interval sequence database
- Example 306 : Calculating statistics for a multi-dimensional sequence database
- Example 307 : Calculating statistics for a multi-dimensional sequence database with timestamps
- Example 308 : Calculating statistics for a file with double vectors (instances) for clustering
- Example 309 : Calculating statistics for time series
Dataset viewer tools
- Example 310 : View the content of an ARFF file using the ARFF Viewer
- Example 311 : View a graph database file with the Graph Viewer
- Example 312 : View an event sequence with the Event Sequence Viewer
- Example 313 : View a sequence database file with the Sequence Database Viewer
- Example 314 : View a sequence database cost binary utility file with the Sequence Database Cost Binary Utility Viewer
- Example 315 : View a sequence database cost numeric utility file with the Sequence Database Cost Numeric Utility Viewer
- Example 316 : View a time-extended sequence database with the Time-Extended Sequence Database Viewer
- Example 317 : View an MD sequence database with the MD Sequence Database Viewer
- Example 318 : View an MD time sequence database with the MD Time Sequence Database Viewer
- Example 319 : View a sequence utility database file with the Sequence Utility Database Viewer
- Example 320 : View a transaction database file with the Transaction Database Viewer
- Example 321 : View an uncertain transaction database file with the Uncertain Transaction Database Viewer
- Example 322 : View a utility transaction database file with the Utility Transaction Database Viewer
- Example 323 : View a utility time transaction database file with the Utility Time Transaction Database Viewer
- Example 324 : View a utility period transaction database file with the Utility Period Transaction Database Viewer
- Example 325 : View a product transaction database file with the Profit Transaction Database Viewer
- Example 326 : View a cost utility transaction database file with the Cost Utility Transaction Database Viewer
- Example 327 : View a Time-Interval Sequence Database with the Time Interval Sequence Database Viewer
Other GUI tools
- Example 328 : Open the Algorithm Explorer to view informations about algorithms
- Example 329 : Create a workflow using the SPMF Workflow editor
- Example 330 : Monitor the JVM's Memory using the Memory Viewer
- Example 331 : Visualize patterns in a text file with the Pattern Viewer
- Example 332 : Open a text file using the SPMF text editor
- Example 333 : Open a text file with the System Text Editor
- Example 334 : Open the SPMF developers tools
- Example 335 : Download an offline copy of the SPMF documentation on your computer
Experiments
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