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  1. University of Computer Studies, Yangon
  2. Conferences

Implementation of Sequential Pattern Mining with Item Intervals

http://hdl.handle.net/20.500.12678/0000004007
http://hdl.handle.net/20.500.12678/0000004007
9a6c61ad-2d8e-4701-a7cb-666f86f215cd
e13daa5e-aa7c-4a37-9fdd-e0d7c9507067
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55117.pdf 55117.pdf (567 Kb)
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Article
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Publication
Title
Title Implementation of Sequential Pattern Mining with Item Intervals
Language en
Publication date 2009-12-30
Authors
Kyaw, Kay Zar
Hla, Ni Ni
Description
Sequential Pattern mining is an important data mining field with wide range of applications that can extract frequent sequences while maintaining their order. It is important to identify item intervals of sequential patterns extracted by sequential pattern mining. There are two approaches for integration of item intervals with sequential pattern mining; constraint-based mining and extended sequence-based mining. This paper presents the combination of those two item interval approaches. PrefixSpan algorithm is used to find the frequent sequence patterns from the sequence database. PrefixSpan algorithm overcomes the problems of Apriori-based algorithms since it avoids the candidate generation and multiple database scanning time. Moreover, prefix-projectiong substantially reducest the size of projected databases and leads to efficient processing.
Keywords
Data Mining, Web Mining, Frequent Patterns, Sequential Pattern, Apriori, AprioriAll, PrefixSpan, Pattern Growth Method
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1719
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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