SCS: A New Similarity Measure for Categorical Sequences

Abstract

Measuring the similarity between categorical sequences is a fundamental process in many data mining applications. A key issue is to extract and make use of significant features hidden behind the chronological and structural dependencies found in these sequences. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but such sequences often have similar structural features in chronologically different positions. In this paper we propose SCS, a novel method for measuring the similarity between categorical sequences, based on an original pattern matching scheme that makes it possible to capture chronological and non-chronological dependencies. SCS captures significant patterns that represent the natural structure of sequences, and reduces the influence of those representing noise. It constitutes an effective approach for measuring the similarity of data such as biological sequences, natural language texts and financial transactions. To show its effectiveness, we have tested SCS extensively on a range of datasets, and compared the results with those obtained by various mainstream algorithms.