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The challenges of Clustering techniques for High Dimensional Data

Abstract: Clustering analysis divides data into groups (clusters) for the purposes of summarization or improved understanding. For example,cluster analysis has been used to group related documents for browsing, to find genes and proteins that have similar functionality,or as a means of data compression. While clustering has a long history and a large number of clustering techniques have been developed in statistics, pattern recognition, data mining,and other fields, significant challenges still remain.In this paper provide a short introduction to cluster analysis, and then focus on the challenge of clustering high dimensional data . Cluster analysis is a challenging task and there are a number of well-known issues associated with it , e.g., finding clusters in data where there are clusters of different shapes ,sizes and density or where the data has lots of noise and outliers. These issues become more important in the context of high dimensionality data sets. Clustering depends critically on density and distance (similarity) ,but these concepts become increasingly more difficult to define as dimensionality increases.

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