Finding Frequent Patterns Using Length-Decreasing Support Constraints

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 27, 2003
Accession Number
ADA439583

Entities

People

  • George Karypis
  • Masakazu Seno

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Computations
  • Computer Science
  • Computers
  • Data Mining
  • Databases
  • Demographic Cohorts
  • Equations
  • Frequency
  • High Performance Computing
  • Intervals
  • Observation
  • Scanning
  • Sequences
  • Test And Evaluation
  • Trees (Data Structures)

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms