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An Efficient Technique for Sequential Rule Mining

Author(s): Bhupendra Mandloi

Journal: Journal of Current Engineering Research
ISSN 2250-2637

Volume: 2;
Issue: 5;
Date: 2012;
Original page

Keywords: In this paper | we presented a novel algorithm for mining sequential rules common to several sequences. Unlike previous algorithms | it does not use a generate-candidate-and-test approach. Instead | it uses a pattern-growth approach for discovering valid r

Tremendous amount of data being collected is increasing speedily by computerized applications around the world. Hidden in the vast data, the valuable information is attracting researchers of multiple disciplines to study effective approaches to derive useful knowledge from within. Among various data mining objectives, the mining of frequent patterns has been the focus of knowledge discovery in databases. This thesis aims to investigate efficient algorithm for mining including association rules and sequential patterns. Mining sequential patterns with time constraints, such as time gaps and sliding time-window, may reinforce the accuracy of mining results. However, the capabilities to mine the time-constrained patterns were previously available only within Apriori framework. Recent studies indicate that pattern-growth methodology could speed up sequence mining. Current algorithms use a generate-candidate-and-test approach that may generate a large amount of candidates for dense datasets. Many candidates do not appear in the database. Therefore we are introducing a more efficient algorithm for sequential rule mining. The time & space consumption of proposed algorithm will be lesser in comparison to previous algorithm.
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