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Modified Stochastic Algorithm for Mining Frequent Subsequences

  • Loreta Savulioniene
  • Leonidas Sakalauskas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 403)

Abstract

The task of market basket analysis is one of the oldest areas of data mining, but still remains very relevant in today’s market. Supermarkets have enormous amounts of data about purchases and it is always important to know what items the market basket contains, how it fluctuates, whether it depends on a particular season, etc. In order to solve these tasks various data mining methods and algorithms are applied. One of them is discovering association rules. The article introduces the modified stochastic algorithm for mining frequent subsequences, as well as computer modeling results and conclusions are presented. The essence of the modified stochastic algorithm is to quickly discover frequent subsequences based on the 1-element subsequence discovered by the Apriori algorithm. In the algorithm the database is scanned once, frequent subsequences and association rules are discovered. The confidence of the algorithm is estimated applying probability statistical methods.

Keywords

frequent subsequence association rule Apriori algorithm modified stochastic algorithm for mining frequent subsequences 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Loreta Savulioniene
    • 1
  • Leonidas Sakalauskas
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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