A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I

Abstract

Sequential data mining involves analysis of sequential patterns of varying length. Sequential pattern analysis is important for pattern discovery from sequences of discrete symbols as in bioinformatics and text analysis, and from sequences or sets of continuous valued feature vectors as in processing of audio, speech, music, image, and video data. Pattern analysis techniques using kernel methods have been explored for static patterns as well as sequential patterns. The main issue in sequential pattern analysis using kernel methods is the design of a suitable kernel for sequential patterns of varying length. Kernel functions designed for sequential patterns are known as dynamic kernels. In this chapter, we present a brief description of kernel methods for pattern classification and clustering. Then we describe dynamic kernels for sequences of continuous feature vectors. We then present a review of approaches to sequential pattern classification and clustering using dynamic kernels.