Methods for frequent pattern mining in data streams within the MOA system

IncMine is a robust, efficient, practical, usable and extendable solution to perform Frequent Itemset mining over data streams. It is implementend under the Massive Online Analysis framework. It includes an analysis over its performances and its reaction to synthetic and real concept drift.

[1]  Anand Rajaraman,et al.  Mining of Massive Datasets , 2011 .

[2]  Wilfred Ng,et al.  A survey on algorithms for mining frequent itemsets over data streams , 2008, Knowledge and Information Systems.

[3]  Albert Bifet,et al.  Adaptive learning and mining for data streams and frequent patterns , 2009, SKDD.

[4]  Salvatore Orlando,et al.  Fast and memory efficient mining of frequent closed itemsets , 2006, IEEE Transactions on Knowledge and Data Engineering.

[5]  Mohammad Hadi Sadreddini,et al.  A dynamic layout of sliding window for frequent itemset mining over data streams , 2012, J. Syst. Softw..

[6]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[7]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  Hongye Su,et al.  Approximate mining of global closed frequent itemsets over data streams , 2011, J. Frankl. Inst..

[10]  Suh-Yin Lee,et al.  Mining frequent itemsets over data streams using efficient window sliding techniques , 2009, Expert Syst. Appl..

[11]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Association Rule Mining , 2007 .

[12]  Won Suk Lee,et al.  estWin: adaptively monitoring the recent change of frequent itemsets over online data streams , 2003, CIKM '03.

[13]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[14]  Ricard Gavaldà,et al.  Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.

[15]  Toon Calders,et al.  Mining Frequent Itemsets in a Stream , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[16]  Seyed Mostafa Fakhrahmad,et al.  An Efficient Frequent Itemset Mining Method over High-speed Data Streams , 2012, Comput. J..

[17]  Geoff Holmes,et al.  MOA: Massive Online Analysis , 2010, J. Mach. Learn. Res..

[18]  J. Shane Culpepper,et al.  Efficient set intersection for inverted indexing , 2010, TOIS.

[19]  Wonsuk Lee,et al.  Finding maximal frequent itemsets over online data streams adaptively , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[20]  Nan Jiang,et al.  CFI-Stream: mining closed frequent itemsets in data streams , 2006, KDD '06.

[21]  Philip S. Yu,et al.  Catch the moment: maintaining closed frequent itemsets over a data stream sliding window , 2006, Knowledge and Information Systems.

[22]  Wilfred Ng,et al.  Maintaining frequent closed itemsets over a sliding window , 2008, Journal of Intelligent Information Systems.

[23]  Yue-Shi Lee,et al.  A fast algorithm for mining frequent closed itemsets over stream sliding window , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[24]  Kenneth O. Stanley Learning Concept Drift with a Committee of Decision Trees , 2003 .

[25]  Jian Pei,et al.  On computing condensed frequent pattern bases , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[26]  Engelbert Mephu Nguifo,et al.  Frequent closed itemset based algorithms: a thorough structural and analytical survey , 2006, SKDD.

[27]  Bruce Eckel Thinking in Java , 1998 .

[28]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[29]  Mohammed J. Zaki Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[30]  Donald E. Knuth,et al.  The Art of Computer Programming: Volume 3: Sorting and Searching , 1998 .