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Knowledge discovery of temporal databases

University of British Columbia

This thesis deals with a problem of mining sequential patterns from temporal databases. Data Mining is the non-trivial extraction of implicit, previously unknown, and potentially useful information from data. A sequential pattern, is an ordered list (ordered by increasing event's time) of events. The main contribution of this thesis is the development of a general model of sequential patterns as well as algorithms for discovering such patterns. We investigate the generalized sequential pattern in which time isn't just part of the constraints but also a part of the discovered pattern. We have developed several algorithms to discover the generalized sequential patterns from a given temporal database. We have implemented four of these algorithms in C++. The results we obtained from applying these implementations to various datasets are matching our expectations.

Thesis/Dissertation

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2009-07-09

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http://hdl.handle.net/2429/10512

Computer Science

University of British Columbia

UBCV

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