Finding frequent patterns using length-decreasing support constraints

Masakazu Seno, George Karypis

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Finding prevalent patterns in large amount of data has been one of the major problems in the area of data mining. Particularly, the problem of finding frequent itemset or sequential patterns in very large databases has been studied extensively over the years, and a variety of algorithms have been developed for each problem. The key feature in most of these algorithms is that they use a constant support constraint to control the inherently exponential complexity of these two problems. In general, patterns that contain only a few items will tend to be interesting if they have a high support, whereas long patterns can still be interesting even if their support is relatively small. Ideally, we want to find all the frequent patterns whose support decreases as a function of their length without having to find many uninteresting infrequent short patterns. Developing such algorithms is particularly challenging because the downward closure property of the constant support constraint cannot be used to prune short infrequent patterns. In this paper we present two algorithms, LPMiner and SLPMiner. Given a length-decreasing support constraint, LPMiner finds all the frequent itemset patterns from an itemset database, and SLPMiner finds all the frequent sequential patterns from a sequential database. Each of these two algorithms combines a well-studied efficient algorithm for constant-support- based pattern discovery with three effective database pruning methods that dramatically reduce the runtime. Our experimental evaluations show that both LPMiner and SLPMiner, by effectively exploiting the length-decreasing support constraint, are up to two orders of magnitude faster, and their runtime increases gradually as the average length of the input patterns increases.

Original languageEnglish (US)
Pages (from-to)197-228
Number of pages32
JournalData Mining and Knowledge Discovery
Issue number3
StatePublished - May 2005

Bibliographical note

Funding Information:
∗This work was supported by NSF CCR-9972519, EIA-9986042, ACI-9982274, ACI-0133464, and by Army High Performance Computing Research Center contract number DA/DAAG55-98-1-0441. Access to computing facilities was provided by the Minnesota Supercomputing Institute.


  • Association rules
  • Data-mining
  • Frequent pattern discovery
  • Scalability


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