Pattern Mining: The Online Course (BETA)

Philippe Fournier-Viger
Distinguished professor, Ph.D.
https://www.philippe-fournier-viger.com

Introduction

Welcome! Do you want to learn how to find hidden patterns in data that can help you understand it better and make better decisions?

If yes, then this free online course on pattern mining is for you!

Pattern mining is a subfield of data mining that aim at applying algorithms to discover interesting patterns in data. These patterns can be used to understand the data or to support decision-making or tasks such as prediction.

This course is designed to introduce students or researchers to the different topics of pattern mining, and explain the key algorithms and key concepts.

By taking this course, you will:

Learn the theory and key algorithms in pattern mining from a leading researcher in the field, and founder of the popular, SPMF sofware.
Explore various topics and applications of pattern mining, such as frequent itemset mining, sequential pattern mining, episode mining, and periodic pattern mining.
Gain practical skills and experience by using the SPMF software to apply pattern mining techniques to real-world datasets.
Access all resources for this course, for free.

How to study?

This course is an online course that consists of multiple recorded lectures that you can watch. After watching a lecture, you can do the corresponding exercises to test your knowledge.

In general, it is not necessary to watch all the content. You may skip some videos if you are not interested by some topics.

Because this is a beta version of the course, I will keep improving the course with more content over time.

Hope you will enjoy the course. If you have any comments or suggestions, you may send me an e-mail or post a message in the data mining forum.

Topic	Lectures	Exercises
1	Introduction Lecture(s) Welcome to the course (video - 1 min ) Introduction to data mining (pdf / ppt / video - 45 min)	Questions about data mining
2	Frequent itemset mining and association rule mining Lecture(s) Frequent itemset mining and the Apriori Algorithm (pdf / ppt / video - 63 min) The Eclat algorithm (pdf / ppt / video - 37 min) Association analysis (pdf / ppt / video - 48 min) Some interesting papers Fournier-Viger, P., Lin, J. C.-W., Vo, B, Chi, T.T., Zhang, J., Le, H. B. (2017). A Survey of Itemset Mining. WIREs Data Mining and Knowledge Discovery, Wiley, e1207 doi: 10.1002/widm.1207, 18 pages. Luna, J. M., Fournier-Viger, P., Ventura, S. (2019). Frequent Itemset Mining: a 25 Years Review. WIREs Data Mining and Knowledge Discovery, Wiley, 9(6):e1329. DOI: 10.1002/widm.1329 Some online tools To test some of the concepts from this lecture, you may try some of these online tools: An online tool that gives the list of all itemsets that can be made from a set of items. An online tool that demonstrates the Apriori algorithm step by step. An online tool that demonstrate how an horizontal database is transformed into a vertical database. An online tool that demonstrates the Eclat algorithm step by step. An online tool that gives the list of all association rules that can be made from a set of items. An online tool to calculate the number of possible itemsets and association rules that can be made from a given number of items.	Questions about frequent itemsets and association rules
3	Concise representations of patterns Lecture(s) Maximal, closed and generator itemsets (pdf / ppt / video - 50 min) Some online tool(s): An online tool that uses a brute-force approach to find all frequent itemsets, closed and maximal frequent itemsets in a dataset (inefficient, but useful for quick testing).	Questions about concise representations of itemsets
4	Rare Pattern Mining Lecture(s) Rare itemset mining (the AprioriRare and AprioriInverse algorithms) (pdf / ppt / video - 38 min)	Questions about rare patterns
5	Correlated and statistically significant patterns Lecture(s) Correlated and statistically significant itemsets (pdf / ppt / video - 49 min ) Some online tool(s): An online tool that uses a brute-force approach to find all frequent itemsets in a transaction dataset and calculate their support, bond and all-confidence (inefficient, but useful for quick testing).	Questions about correlated and statistically significant pattern mining
6	High Utility Itemset Mining Lecture(s) High utility itemset mining (pdf / video - 18 min) The HUI-Miner and FHM algorithms (pdf / video - 45 min) Some interesting paper(s) Fournier-Viger., P., Lin, J. C.-W., Truong, T., Nkambou, R. (2019). A survey of high utility itemset mining. In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer (to appear), p. 1-46. Fournier-Viger, P., Lin. J. C.-W., Vo, B., Nkambou, R., Tseng, V. S. (editors). (2019) High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer. Truong, T., Fournier-Viger., P. (2019). A survey of high utility sequential pattern mining. In: Fournier-Viger et al. (eds). High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer, p. 97-130. Some online tool(s): An online tool that shows how to calculate the utility of an itemset. An online tool that shows how to calculate the utility-list of an itemset.	Questions about high utility itemsets
7	Sequential pattern mining Lecture(s) An Introduction to Sequential Pattern Mining (pdf / ppt / video - 23 min) The PrefixSpan algorithm (pdf / ppt / video - 32 min) Some interesting paper(s) Fournier-Viger, P., Lin, J. C.-W., Kiran, R. U., Koh, Y. S., Thomas, R. (2017). A Survey of Sequential Pattern Mining. Data Science and Pattern Recognition (DSPR), vol. 1(1), pp. 54-77. Tutorial: Using the SPMF software to discover frequent patterns in text documents (tutorial)	Questions about sequential pattern mining
8	Sequential rule mining Lecture(s) An Introduction to Sequential Rule Mining (pdf / ppt / video - 33 min ) The CMRules algorithm (pdf / ppt / video - 32 min)	Questions about sequential rule mining
9	Episode Mining Lecture(s) An Introduction to Episode Mining (pdf / ppt / video - 68 min ) Some interesting paper(s) Ouarem, O., Nouioua, F., Fournier-Viger, P. (2023). A Survey of Episode Mining. WIREs Data Mining and Knowledge Discovery	Questions about episode mining
10	Periodic pattern mining Lecture(s) An Introduction to Periodic Pattern Mining (pdf / ppt / video - 34 min)	Questions about periodic pattern mining
11	Other topics Lecture(s) Approximate pattern mining Frequent subgraph mining (pdf / ppt / video - 11 min) Interactive pattern mining Classification using patterns ....	Questions about frequent subgraph mining
12	...	...

Software, source code and datasets

To try the different pattern mining algorithms discussed in this course, you can download the SPMF data mining software. SPMF is an open-source software, offering over 250 algorithms. It is implemented in Java and there exist also unofficial wrappers for some other languages. Besides, you can find several public datasets to try the algorithms from SPMF on the datasets page of SPMF

FAQ about this course

How can I contact you if I find some error in the course?
- Send me an e-mail with your feedback and suggestions. I will try to fix the errors as soon as possible. You will be listed as a contributor on this webpage.
Where can I get more information about these topics, and also ask questions?
- This webpage lists several resources and you can also find more pattern mining videos on my Youtube Channel. Besides, you can try the different algorithms discussed in this course by using the SPMF software, which is free and open-source. Also if you have question, you can also post your questions in the data mining forum. I check this forum every few days and will try to answer your questions.
Can I use and modify your Powerpoints to teach a course at my university
- Yes, I will be very happy about this! The goal of this free course is to share knowledge. But if you reuse my powerpoints, I ask you to cite this website in your modified PPT and indicate that your powerpoint is based on my content.

Bibliography

This course is based on content from research articles mentioned in the PPTs and PDFs and also some information from those books:

Fournier-Viger, P., Lin. J. C.-W., Vo, B., Nkambou, R., Tseng, V. S. (editors). (2019) High-Utility Pattern Mining: Theory, Algorithms and Applications, Springer.
Han and Kamber (2011), Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann Publishers,
Tan, Steinbach & Kumar (2006), Introduction to Data Mining, Pearson education, ISBN-10: 0321321367.
Data Mining: The Textbook by Aggarwal (2015)
Data Mining and Analysis Fundamental Concepts and Algorithms by Zaki & Meira (2014)

Contributors

Several people have given feedback, ideas or reported errors, related to this course:

Chongsheng Zhang
Wensheng Gan
Tai Dinh
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