Abstract. Predictive clustering is a general framework that unifies clustering and prediction. This paper investigates how to apply this framework to cluster time series data. The resulting system, Clus-TS, constructs predictive clustering trees (PCTs) that partition a given set of time series into homogeneous clusters. In addition, PCTs provide a symbolic description of the clusters. We evaluate Clus-TS on time series data from microarray experiments. Each data set records the change over time in the expression level of yeast genes in response to a change in environmental conditions. Our evaluation shows that Clus-TS is able to cluster genes with similar responses, and to predict the time series based on the description of a gene. Clus-TS is part of a larger project where the goal is to investigate how global models can be combined with inductive databases. 1

In the past decade there have been advance in technologies, the amount of biological data such as DNA sequences and microarray data have been increased tremendously. To obtain knowledge from the data, explore relationships between genes, understanding severe diseases and development of drugs for patterns from the databases of large size and high dimensionality. Information retrieval and data mining are powerful tools to extract information from the databases and/or information repositories. The integrative cluster analysis of both clinical and gene expression data has shown to be an effective alternative to overcome the abovementioned problems. In this paper, we focus on how to improve the searching and the clustering performance in genomic data from commonly used clustering techniques. In the proposed gene clustering technique, firstly, the high dimensionality of the microarray gene data is reduced using LPP. The LPP is chosen for the dimensionality reduction because of its ability of preserving locality of neighborhood relationship. Secondly, through performance experiments on real data sets, the proposed method fuzzy C-means is shown to achieve higher efficiency, clustering quality and automation than other clustering method.