CNclustering: Clustering with compatible nucleoids

Abstract

Dissimilarity measure plays a very important role in traditional data clustering. In this paper, we extend the dissimilarity measure as compatible measure and present a new algorithm (CNclustering) based on this measure. The algorithm is a rigorous partition method, it first gets some compatible clusters with a Compclustering method as the initial nucleoids, then absorbs other objects by the absorbing step to form the final clusters. We use S20 and S200 data sets to demonstrate the clustering performance of the algorithm and get some consistent results。 Index Terms-clustering algorithm, dissimilarity, nucleoid, compatible relation, absorbing.I.

Compatible Clustering Algorithm with Convex Space Partitioning

“…Firstly, we give a sketch of compatible cluster, further details about these can be found in [1] [8].…”

“…Cluster analysis is a very important tool in data analysis and data processing field, it has a wide range of applications in machine vision, statistics, machine learning and data mining. The aim of cluster analysis is to partition a data set into subsets (clusters) such that members of the same cluster are similar and members of distinct clusters are dissimilar, where the similarity of two data members is usually defined by a distance function [1]. While from the view of objects' relations, dissimilarity is just one kind of measure for the objects relation analysis.…”

“…In [8], Wan et al proposed an algorithm called as CompClustering, which is based on the compatible relation theory. As an extended work of CompClustering, CNclustering algorithm is proposed in [1].…”

An improved compatible clustering algorithm

“…The aim of cluster analysis is to partition a data set into subsets (clusters) such that members of the same cluster are similar and members of distinct clusters are dissimilar, where the similarity of two data members is usually defined by a distance function [1]. While from the view of objects' relations, dissimilarity is just one kind measure for the objects relation analysis.…”