Comprehensive analysis of different clustering algorithm and their impact over time series data

Clustering is an approach to divide data into number of groups on the basis of some mutual characteristics each group called clusters, consists of objects that are similar between themselves and dissimilar to objects between other groups. Nowadays a lot of work is being carried out for trend analysis on time series data set. The genetic algorithm suffers with the problem in case of time series data because they consider each time stamp as a single entity during clustering. Even some algorithm still give good result on some type of time series data set but overall there is no generalized algorithm which considers different type of time series data set.This paper is presents comprehensive analysis over different type of clustering algorithms like k-means, hierarchical, SOM and GMM on two type of time series data, microarray and financial. These algorithms are compared on the following factors: size of data, number of clusters, type of data set, homogeneity score, separation score, silhouette coefficient etc, and presented extensive conclusion on the basis of the performance, quality and accuracy of the generic clustering algorithms.