Abstract

We present a generalization of frequent itemsets allowing for the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifies errortolerant frequent clusters of items in transactional data (customerpurchase data, web browsing data, text, etc.). The algorithm exploits sparseness of the underlying data to find large groups of items that are correlated over database records (rows). The notion of transaction coverage allows us to extend the algorithm and view it as a fast clustering algorithm for discovering segments of similar transactions in binary sparse data. We evaluate the new algorithm on three real-world applications: clustering highdimensional data, query selectivity estimation and collaborative filtering. Results show that the algorithm consistently uncovers structure in large sparse databases that other traditional clustering algorithms fail to find.

Citations

6231	Maximum likelihood from incomplete data via the EM algorithm - Dempster, Laird, et al. - 1977
3340	Pattern Classification and Scene Analysis - Duda, Hart - 1973
2159	Fast algorithms for mining association rules - Agrawal, Srikant - 1994
1953	Mining association rules between sets of items in large databases - Agrawal, Imielinski, et al. - 1993
872	Grouplens An open architecture for collaborative filtering of netnews - Resnick, Iacovou, et al. - 1994
802	Reputation systems - Resnick, Kuwabara, et al.
787	Human Behavior and the Principle of Least-Effort - Zipf - 1949
774	Empirical Analysis of Predictive Algorithms for Collaborative Filtering - Breese, Heckerman, et al. - 1998
531	Multi-interval discretization of continuousvalued attributes for classification learning - Fayyad, Irani - 1993
503	Efficient and effective clustering methods for spatial data mining - Ng, Han - 1994
476	Concrete Mathematics - Graham, Knuth, et al. - 1989
461	Automatic subspace clustering of high dimensional data for data mining applications - Agrawal, Gehrke, et al. - 1998
416	Bayesian classification (AutoClass): Theory and results - Cheeseman, Stutz - 1996
335	BIRCH: An Efficient Data Clustering Method for Very Large Databases - Zhang, Ramakrishnan, et al. - 1996
304	R.: Mining quantitative association rules in large relational tables - Srikant, Agrawal - 1996
262	ROCK: a robust clustering algorithm for categorical attributes - Guha, Rastogi, et al. - 1999
259	New algorithms for fast discovery of association rules - Zaki, Parthasarathy, et al. - 1997
253	Verkamo, “Fast discovery of association rules - Agrawal, Mannila, et al. - 1996
120	Clustering categorical data: An approach based on dynamical systems - Gibson, Kleingberg, et al. - 1998
71	CACTUS: Clustering categorical data using summaries - Ganti, Gehrke, et al. - 1999
67	An experimental comparison of several clustering methods, Microsoft Research Report MSR-TR-98-06 - Meila, Heckerman
52	Efficiently mining long patterns from databases - Jr - 1998
52	Compressed data cubes for olap aggregate query approximation on continuous dimensions - Shanmugasundaram, Fayyad, et al. - 1999
47	Discovering all most specific sentences by randomized algorithms - Gunopulos, Mannila, et al. - 1997
35	Scaling EM (expectationmaximization) clustering to large databases - Bradley, Fayyad, et al. - 1999
28	Density-based indexing for approximate nearest-neighbor queries - Bennett, Fayyad, et al. - 1999
28	Experiences with GroupLens: Making Usenet useful again - Miller, Riedl, et al. - 1997
6	CURE: An efficient algorithm for clustering large databases - Guha, Rastogi, et al. - 1998