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Modified Stochastic Algorithm for Mining Frequent Subsequences

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Book cover Information and Software Technologies (ICIST 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 403))

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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.

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Authors and Affiliations

  1. Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania

    Loreta Savulioniene & Leonidas Sakalauskas

Authors
  1. Loreta Savulioniene
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  2. Leonidas Sakalauskas
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Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Studentu g. 50-313a, 51368, Kaunas, Lithuania

    Tomas Skersys

  2. Centre of Information Systems Design Technologies, Kaunas University of Technology, Studentu st. 50-313a, 51368, Kaunas, Lithuania

    Rimantas Butleris

  3. Kaunas University of Technology, Studentu g. 50-309a, 51368, Kaunas, Lithuania

    Rita Butkiene

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Savulioniene, L., Sakalauskas, L. (2013). Modified Stochastic Algorithm for Mining Frequent Subsequences. In: Skersys, T., Butleris, R., Butkiene, R. (eds) Information and Software Technologies. ICIST 2013. Communications in Computer and Information Science, vol 403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41947-8_19

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  • DOI: https://doi.org/10.1007/978-3-642-41947-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41946-1

  • Online ISBN: 978-3-642-41947-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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