Improved Clustering Technique with Informative Genes for High Dimensional Data
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Abstract
Data mining is often defined as the process of finding hidden information in a database. Cluster analysis is a powerful tool in the study of gene expression data. Clustering is the process of grouping data objects into a set of disjoint classes, called clusters. K-Means clustering algorithm is one of the most frequently used clustering method in data mining, due to its performance in clustering massive data sets. In addition, the number of distance calculation increases exponentially with the increase of dimensionality of data. Microarray data is taken as high dimensional data and the dimension is being reduced with the use of dimension reduction techniques. In clustering gene expression data, the data may contain noise, irrelevant data and missing data. Preprocessing is an essential step to improve clustering. In this thesis, a new algorithm is proposed which handles high dimensional data. The dimension of the dataset is reduced using FastICA. Then, the dataset is partitioned into k equal sets. In each set, the MODE of each dimension is fixed as initial centroids for K-Means and the data points are clustered using K-Means clustering. The proposed algorithm provides better results in terms of accuracy compared to the existing algorithm.
Keywords: Clustering, High Dimensional Data, K-Means, FastICA.
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