A Random Projected Clustering Algorithm Facing High-Dimensional Categorical Data
Lei Wang · Journal of Chinese Computer Systems · 2006
Most of data always exist in high dimensions.From the whole space,the distribution of these data is so separate that it is difficult to find good clusters.Therefore,more and more concerns are placed on how to cluster high-dimensional data.This paper presents a Random Projected Clustering algorithm(RanPC) for categorical data.After selecting related vectors using frequency,the algorithm produces the centers of cluster randomly and chooses good centers according to the clustering effect.This approach expands projected cluster algorithm from numerical space to categorical space.Experiment shows its practicability and effectivity.