论文信息 - Frequent-Itemset Mining Using Locality-Sensitive Hashing

Frequent-Itemset Mining Using Locality-Sensitive Hashing

The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LSH defined over Hamming distance and Jaccard similarity.

Rameshwar Pratap | Debajyoti Bera

[1] Heikki Mannila,et al. Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[2] Piotr Indyk,et al. Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.

[3] H. Mannila,et al. Discovering all most specific sentences , 2003, TODS.

[4] Piotr Indyk,et al. Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[5] Alan M. Frieze,et al. Min-Wise Independent Permutations , 2000, J. Comput. Syst. Sci..

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

[7] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[8] Edith Cohen,et al. Finding interesting associations without support pruning , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[9] Philip S. Yu,et al. Clustering by pattern similarity in large data sets , 2002, SIGMOD '02.

[10] Rajeev Motwani,et al. Beyond Market Baskets: Generalizing Association Rules to Dependence Rules , 1998, Data Mining and Knowledge Discovery.

[11] Ping Li,et al. Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment , 2015, WWW.

[12] Philip S. Yu,et al. An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[13] Rasmus Pagh. Locality-sensitive Hashing without False Negatives , 2016, SODA.