Optimal Number of Clusters for Fast Similarity Search Considering Transformations of Time Varying Data

Abstract

This paper proposes a method of determining the optimal number of clusters dividing the multiple transformations for the purpose of the efficient processing of query against the results of applying the transformations to time series. In this paper, the moving average is used as a transformation for simplicity. The model of query time to the number of clusters is constructed for determining the optimal number of clusters. As the query time could be represented with the concave function of the number of clusters, it is shown that the optimal number of clusters for the best query time can be obtained. The verification experiment confirms the validity of the model constructed. It is revealed that the optimal number of clusters could be determined by the times obtained from a single query execution.