OPTICS: Ordering Points To Identify the Cluster Structure

Wai-Shing HO

Abstract

Cluster analysis is a primary method for database mining. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does not even exist a global parameter setting for accurate clustering result. This paper proposed an algorithm for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering contains information which is equivalent to the density-based clusterings corresponding to a broad range of parameter settings. For medium sized data sets, the cluster-ordering can be represented graphically and for very large data sets, an appropriate visualization technique is suitable for interactive exploration of the intrinsic clustering structure offering additional insights into the distribution and correlation of the data.

Referred Papers