Mining Integrated Sequential Patterns From Multiple Databases

Christie I. Ezeife, Vignesh Aravindan and Ritu Chaturvedi
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Christie I. Ezeife: University of Windsor, Ontario, Canada
Vignesh Aravindan: Royal Bank of Canada, Canada
Ritu Chaturvedi: School of Computer Science, University of Guelph, Ontario, Canada

International Journal of Data Warehousing and Mining (IJDWM), 2020, vol. 16, issue 1, 1-21

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.

Date: 2020
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