Mining Closed Sequential Patterns in Progressive Databases

R. B. V. Subramanyam (), A. Suresh Rao (), Ramesh Karnati (), Somaraju Suvvari () and D. V. L. N. Somayajulu ()
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R. B. V. Subramanyam: Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
A. Suresh Rao: Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
Ramesh Karnati: Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
Somaraju Suvvari: Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India
D. V. L. N. Somayajulu: Department of Computer Science & Engineering, National Institute of Technology Warangal, Andhra Pradesh, India

Journal of Information & Knowledge Management (JIKM), 2013, vol. 12, issue 03, 1-10

Abstract: Previous studies of Mining Closed Sequential Patterns suggested several heuristics and proposed some computationally effective techniques. Like, Bidirectional Extension with closure checking schemas, Back scan search space pruning, and scan skip optimization used in BIDE (BI-Directional Extension) algorithm. Many researchers were inspired with the efficiency of BIDE, have tried to apply the technique implied by BIDE to various kinds of databases; we toofelt that it can be applied over progressive databases. Without tailoring BIDE, it cannot be applied to dynamic databases. The concept of progressive databases explores the nature of incremental databases by defining the parameters like, Period of Interest (POI), user defined minimum support. An algorithm PISA (Progressive mIning Sequential pAttern mining) was proposed by Huang et al. for finding all sequential patterns over progressive databases. The structure of PISA helps in space utilization by limiting the height of the tree, to the length of POI and this issue is also a motivation for further improvement in this work. In this paper, a tree structure LCT (Label, Customer-id, and Time stamp) is proposed, and an approach formining closed sequential patterns using closure checking schemas across the progressive databases concept. The significance of LCT structure is, confining its height to a maximum of two levels. The algorithmic approach describes that the window size can be increased by one unit of time. The complexity of the proposed algorithmic approach is also analysed. The approach is validated using synthetic data sets available in Internet and shows a better performance in comparison to the existing methods.

Keywords: Data mining; sequential patterns; progressive databases; LCT tree structure; closed sequential patterns with sliding window (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S021964921350024X

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