Methods for frequent pattern mining in data streams within the MOA system
暂无分享,去创建一个
[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 .