Optimized Clustering Techniques with Special Focus to Biomedical Datasets

The clinical data including clinical test results, MRI images and drug responses of patients are documented and analyzed with machine learning and data mining tools. The scale and complexity of these datasets is a big challenge to machine learning and data mining community as the data is of mixed type. The extraction of meaningful or desired information from these datasets provides knowledge in decision making process which in turn helps for the diagnosis and treatment of the diseases. Biomedical datasets are a collection of data with diverse types as it involves images, clinical studies, statistical reports etc. The recent researches have focused on different clustering and classification methods to manage and analyze the biomedical datasets. The objective of this chapter is to cluster or classify the patterns of interest from Brain MRI images, Liver disorder and Breast cancer datasets using efficient clustering methodologies. Among the different algorithms in data mining for clustering, classification, visualization and interpretation, K Means, Fuzzy C Means and Neural Networks(NN) are frequently used for clustering and classification of biomedical datasets. The performance of these methods are greatly influenced by the initialization of K value and its convergence speed. This chapter discusses about FCM and K Means clustering methods and its optimization with meta heuristics such as Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO). The experimental section of this paper exhibits analysis in terms of Intra cluster distances, elapsed time and Davis Bouldin Index (DBI).