Mining Integrated Sequential Patterns From Multiple Databases

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Author(s): Christie I. Ezeife (University of Windsor, Ontario, Canada), Vignesh Aravindan (Royal Bank of Canada, Canada) and Ritu Chaturvedi (School of Computer Science, University of Guelph, Ontario, Canada)
Copyright: 2020
Volume: 16
Issue: 1
Pages: 21
Source title: International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: David Taniar (Monash University, Australia)
DOI: 10.4018/IJDWM.2020010101

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Existing work on multiple databases (MDBs) sequential pattern mining cannot mine frequent sequences to answer exact and historical queries from MDBs having different table structures. This article proposes the transaction id frequent sequence pattern (TidFSeq) algorithm to handle the difficult problem of mining frequent sequences from diverse MDBs. The TidFSeq algorithm transforms candidate 1-sequences to get transaction subsequences where candidate 1-sequences occurred as (1-sequence, itssubsequenceidlist) tuple or (1-sequence, position id list). Subsequent frequent i-sequences are computed using the counts of the sequence ids in each candidate i-sequence position id list tuples. An extended version of the general sequential pattern (GSP)-like candidate generates and a frequency count approach is used for computing supports of itemset (I-step) and separate (S-step) sequences without repeated database scans but with transaction ids. Generated patterns answer complex queries from MDBs. The TidFSeq algorithm has a faster processing time than existing algorithms.

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