Feature Selection for Clustering

Clustering is an important data mining task Data mining often concerns large and high dimensional data but unfortunately most of the clustering algorithms in the literature are sensitive to largeness or high dimensionality or both Di erent features a ect clusters di erently some are important for clusters while others may hinder the clustering task An e cient way of handling it is by selecting a subset of important features It helps in nding clusters e ciently understanding the data better and reducing data size for e cient storage collection and process ing The task of nding original important features for unsupervised data is largely untouched Traditional feature selection algorithms work only for supervised data where class information is available For unsuper vised data without class information often principal components PCs are used but PCs still require all features and they may be di cult to understand Our approach rst features are ranked according to their importance on clustering and then a subset of important features are selected For large data we use a scalable method using sampling Em pirical evaluation shows the e ectiveness and scalability of our approach for benchmark and synthetic data sets