Threshold based similarity clustering of medical data

Abstract

Due to increase in number of technologies, a large amount of data gets accumulated. The need arises to handle this data for retrieving and analyzing useful information. Clustering of temporal data has been explored using evolutionary clustering. However the time dimension associated with the record has not been considered. Traditional clustering algorithms usually focus on grouping data objects based on similarity function. Temporal data clustering extends traditional clustering mechanisms and provides underpinning solutions for discovering the evolving information over the period of time. This paper proposes a methodology for clustering medical observations of patients based on a new similarity measure. We show how to accelerate the clustering algorithm by avoiding unnecessary distance calculations by applying such similarity measure.