On mining progressive positive and negative sequential patterns simultaneously

Jen Wei Huang, Yong Bin Wu, Bijay Prasad Jaysawal

Research output: Contribution to journalArticlepeer-review

Abstract

Positive sequential pattern (PSP) mining focuses on appearing items, while negative sequential pattern (NSP) mining tends to find the relationship between occurring and non-occurring items. There are few works involved in NSP mining, and the definitions of NSP are inconsistent in each work. The support threshold for PSP is always applied on NSP, which cannot bring out interesting patterns. In addition, PSP has been discovered on incremental databases and progressive databases, while NSP mining is only performed on static databases. Progressive sequential pattern mining finds the most up-to-date patterns, which can provide more valuable information. However, the previous progressive sequential pattern mining algorithm contains some redundant process. In this paper, we aim to find NSP on progressive databases. A new definition of NSP is given to discover more meaningful and interesting patterns. We propose an algorithm, Propone, for efficient mining process. We also propose a level-order traversal strategy and a pruning strategy to reduce the calculation time and the number of negative sequential candidates (NSC). By comparing Propone with some modified previous algorithms, the experimental results show that Propone outperforms comparative algorithms.

Original languageEnglish
Pages (from-to)145-169
Number of pages25
JournalJournal of Information Science and Engineering
Volume36
Issue number1
DOIs
Publication statusPublished - 2020 Jan 1

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics

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