A closer look into sequential clustering algorithms and associated post-processing refinement strategies. (1st January 2014) - Real, Eduardo Machado; do Carmo Nicoletti, Maria; de Oliveira, Osvaldo Luiz
Clustering refers to a group of unsupervised classification techniques which, generally, only rely on the available data patterns to infer groups of similar patterns. Among the many available clustering algorithms the so called sequential clustering algorithms have been characterised as fast and straightforward methods which produce, as result, a single clustering. They have the drawback of being dependent on the order in which data patterns are input to the algorithm and, generally, produce compact and spherical clusters. The focus of the work is the empirical investigation of a group of three sequential algorithms namely the basic sequential algorithmic scheme (BSAS) and two of its variations, the MBSAS and the TTSAS. The work also investigates the use of three refinement strategies which aim to improve the performance of the three sequential algorithms considered, which are based on two procedures: merge and reassignment. Results from experiments conducted in various data domains (from UCI and synthetic) are presented and a comparative analysis is given as evidence of the benefits of sequential clustering algorithm coupled with a refinement procedure.