No abstract

Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast and accurate clustering. Its performance is compared with the traditional K-means & KHM algorithm. The result obtained from proposed hybrid algorithm is much better than the traditional K-mean & KHM algorithm.

Order-preserving sub matrices (OPSM's) have been shown useful in capturing concurrent patterns in data when the relative magnitudes of data items are more important than their correct values. For example, in analyzing gene expression profiles obtained from micro-array experiments, the comparative magnitudes are important both since they represent the change of gene activities across the experiments, and since there is naturally a high level of noise in data that makes the exact values non trustable. To manage with data noise, repeated experiments are often conducted to collect multiple measurements. This paper includes Eigen value decomposition combined for solving data mining from order preserving sub-matrices from repeated dataset. Experimental results shows this method gives far better results in terms of time and candidate pattern ratio.