Constrained frequent pattern mining: a pattern-growth view

By Jian Pei, Jiawei Han. 2002

166 citations recorded by the source corpus.

It has been well recognized that frequent pattern mining plays an essential role in many important data mining tasks. However, frequent pattern mining often generates a very large number of patterns and rules, which reduces not only the efficiency but also the effectiveness of mining. Recent work has highlighted the importance of the constraint-based mining paradigm in the context of mining frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases.Recently, we developed efficient pattern-growth methods for frequent pattern mining. Interestingly, pattern-growth methods are not only efficient but also effective in mining with various constraints. Many tough constraints which cannot be handled by previous methods can be pushed deep into the pattern-growth mining process. In this paper, we overview the principles of pattern-growth methods for constrained frequent pattern mining and sequential pattern mining. Moreover, we explore the power of pattern-growth methods towards mining with tough constraints and highlight some interesting open problems.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “There is also a rich literature on constraint-based pattern mining [52], [53]”
Sequential pattern mining -- approaches and algorithms (2013, background): “Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org.”