From similarity retrieval to cluster analysis: The case of R*-trees

Abstract

Data mining is concerned with important aspects related to both database techniques and AI/machine learning mechanisms, and provides an excellent opportunity for exploring the interesting relationship between retrieval and inference/reasoning, a fundamental issue concerning the nature of data mining. In the data mining context, this relationship can be restated as connection and differences between data retrieval and data mining. In this paper we explore this relationship by examining time series data indexed through R*-trees, and study the issues of (1) retrieval of data similar to a given query (which is a plain data retrieval task), and (2) clustering of the data based on similarity (which is a data mining task). Along the way of examination of our central theme, we also report new algorithms and new results related to these two issues. We have developed a software package consisting of a similarity analysis tool and two implemented clustering algorithms: KMeans-R and Hierarchy-R. A sketch of experimental results is also provided