A Representative Set Method for Symbolic Sequence Clustering

Abstract

Sequence decomposition into a set of consecutive, distinct subsequences is crucial for symbolic sequence analysis. It reduces significantly the reference base of the recorded sequence for further retrieval and allows for original similarity and membership measures of the sequences. The introduced measures are a start point to a new algorithm for clustering sequences into groups of similar individuals. Algorithms that use the concept of a representative set achieved relatively good clustering results. The representative set that we have introduced is precisely and uniquely defined in contrast to that used in other applications.

A New Method for Symbolic Sequences Analysis. An Application to Long Sequences

“…In [12] a method for sequence clustering that is based on a representative set of strings was published. In the present paper a more efficient and reliable method for sequence clustering than that presented in [12] is proposed.…”

“…In the present paper a more efficient and reliable method for sequence clustering than that presented in [12] is proposed. It uses the similarity measure for the preparation of input centroids to K-means algorithm and a distance measure for the detection of the optimal set of centroids and clusters.…”