LPMiner: An algorithm for finding frequent itemsets using length-decreasing support constraint

Masakazu Seno, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

52 Scopus citations


Over the years, a variety of algorithms for finding frequent itemsets in very large transaction databases have been developed. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of the problem. In general, itemsets that contain only a few items will tend to be interesting if they have a high support, whereas long itemsets can still be interesting even if their support is relatively small. Ideally, we desire to have an algorithm that finds all the frequent itemsets whose support decreases as a function of their length. In this paper we present an algorithm called LPMinel, that finds all itemsets that satisfy a length-decreasing support constraint. Our experimental evaluation shows that LPMiner is up to two orders of magnitude faster than the FP-growth algorithm for finding itemsets at a constant support constraint, and that its runtime increases gradually as the average length of the transactions (and the discovered itemsets) increases.

Original languageEnglish (US)
Title of host publicationProceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
Number of pages8
StatePublished - Dec 1 2001
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: Nov 29 2001Dec 2 2001

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other1st IEEE International Conference on Data Mining, ICDM'01
Country/TerritoryUnited States
CitySan Jose, CA

Cite this