An extended affinity propagation clustering method based on different data density types.

Xiuli Zhao,Weixiang Xu

doi:10.1155/2015/828057

Abstract

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

Highlights

Affinity propagation (AP) is a new partitioning clustering algorithm proposed by Frey and Dueck in 2007 [1]
In this paper we propose an extended AP clustering algorithm that can cluster data point set into clusters according to their different density types
There are two novelties in our new extended AP clustering algorithm: (1) according to the frequency distribution curve of the Computational Intelligence and Neuroscience nearest neighbor distance, it can identify the number of the different density types in the whole data set; (2) it can partition the data set into clusters more effectively than the OPTICS and AP clustering algorithm itself

Summary

Introduction

Affinity propagation (AP) is a new partitioning clustering algorithm proposed by Frey and Dueck in 2007 [1]. AP algorithm assigns each data point to its nearest exemplar, which results in a partitioning of the whole data set into some clusters. In this paper we propose an extended AP clustering algorithm that can cluster data point set into clusters according to their different density types. Nearest neighbor distance, it can identify the number of the different density types in the whole data set; (2) it can partition the data set into clusters more effectively than the OPTICS and AP clustering algorithm itself. In this data set, there are some unknown data density types, and each of them may be nested to other clusters with other density types.

Related Work

Related Work on the Nearest Neighbor Cluster Method

Some Concepts Relating to AP Clustering Algorithm

The Extended AP Clustering Algorithm and Experiments

Experiments and Analysis

Conclusion and Future Work