An efficient closed frequent itemset miner for the MOA stream mining system

We describe and evaluate an implementation of the IncMine algorithm due to Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams, working on the MOA platform. The goal was to produce a robust, efficient, and usable tool for that task that can both be used by practitioners and used for evaluation of research in the area. We experimentally confirm the excellent performance of the algorithm and its ability to handle concept drift.

[1]  Hilla Peretz,et al.  Ju n 20 03 Schrödinger ’ s Cat : The rules of engagement , 2003 .

[2]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[3]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

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

[5]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[6]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[7]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Itemset Mining , 2002, SDM.

[8]  Peter Jackson,et al.  Lord of the rings: the fellowship of the ring , 2002, SIGGRAPH '02.

[9]  Judith Weaver.  Star wars: episode II "attack of the clones" , 2002, SIGGRAPH Electronic Art and Animation Catalog.

[10]  Mohammed J. Zaki,et al.  Fast vertical mining using diffsets , 2003, KDD '03.

[11]  Jian Pei,et al.  CLOSET+: searching for the best strategies for mining frequent closed itemsets , 2003, KDD '03.

[12]  D. Mcbride,et al.  Chapter Five. Gothic and the family: The Mummy Returns, Harry Potter and the Philosopher’s Stone, andThe Lord of the Rings: The Fellowship of the Ring , 2002, Screening the Gothic.

[13]  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).

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

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

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

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

[18]  Baihua Zheng,et al.  CLAIM: An Efficient Method for Relaxed Frequent Closed Itemsets Mining over Stream Data , 2007, DASFAA.

[19]  Diana Eastment,et al.  Pirates of the Caribbean: The Curse of the Black Pearl , 2008 .

[20]  Suh-Yin Lee,et al.  Incremental updates of closed frequent itemsets over continuous data streams , 2009, Expert Syst. Appl..

[21]  Young-Koo Lee,et al.  Sliding window-based frequent pattern mining over data streams , 2009, Inf. Sci..

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

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

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

[25]  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).

[26]  Carson Kai-Sang Leung,et al.  Frequent itemset mining of uncertain data streams using the damped window model , 2011, SAC.

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

[28]  O. P. Vyas,et al.  Data Stream Mining: A Review , 2013 .

[29]  BifetAlbert,et al.  An efficient closed frequent itemset miner for the MOA stream mining system , 2015 .