Cluster Analysis of Temporal Data using Maximum Likelihood Estimation

Abstract

Due to rapid growth of technologies, a large amount of data gets generated. 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. However, if temporal dimension is incorporated, it allows to perform cluster analysis for evolving patterns. Temporal data clustering extends traditional clustering mechanisms and provides underpinning solutions for discovering the condensing information over the period of time. This paper proposes a methodology for clustering records based on time frame. The proposed methodology first clusters the records based on time frame. When a new query record comes, using maximum likelihood estimation we try to identify its true representative cluster. The assignment of query record to a particular cluster is based on the distance measure.