Inducing sequential patterns from multidimensional time series data

Authors:
Chang-Hwan Lee
Affiliations:
Department of Information and Communications, DongGuk University, Seoul, Korea
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

Inducing sequential patterns from time series data is an important data mining problem. While most of the current methods are generating sequential patterns within a single attribute, this paper proposes a new method, using Hellinger entropy measure, for generating multi-dimensional sequential patterns. A number of theorems are proposed to reduce the computational complexity of the proposed method.