Article ID Journal Published Year Pages File Type
418016 Computational Statistics & Data Analysis 2008 11 Pages PDF
Abstract

The detection of patterns in categorical time series data is an important task in many fields of science. Several efficient algorithms for finding frequent sequential patterns have been proposed. An online-approach for sequential pattern analysis based on transforming the categorical alphabet to real vectors and generating fractals by an iterated function systems (IFS) is suggested. Sequential patterns can be analyzed with standard methods of cluster analysis using this approach. A version of the procedure allows detecting patterns visually.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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