Improved Ant Colony Clustering Algorithm and Its Performance Study.

Wei Gao

doi:10.1155/2016/4835932

Abstract

Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.

Highlights

Clustering divides data into homogeneous subgroups, with some details disregarded to simplify the data
The computational accuracy of the abstraction ant colony clustering algorithm is superior to the ant colony clustering algorithm and the K-modes clustering algorithm
The computational accuracy of the abstraction ant colony clustering algorithm is superior to the ant colony clustering algorithm and the K-means clustering algorithm

Summary

Introduction

Clustering divides data into homogeneous subgroups, with some details disregarded to simplify the data. Because ants move randomly and spend significant time finding proper places to drop or pick up objects, the computational efficiency and accuracy of ant colony clustering algorithms are low, for large and complicated engineering problems. To overcome these shortcomings, a new abstraction ant colony clustering algorithm is proposed that uses a data combination mechanism. {0, otherwise, where f(oi) is the average similarity of data oi to other data objects oj (oj ≠ oi, oj ∈ cluster) in the current data reactor, s is the number of data objects in the data reactor visited by the current ant, cluster(oi) is the data reactor that the data object oi belongs to, d(oi, oj) is the Euclidean distance, and α defines a parameter used to adjust the similarity between objects. Because the combination mechanism for data reactors used in the proposed new algorithm is the abstraction of clustering mechanism of similar data used in traditional ant colony clustering algorithms, the proposed new algorithm is called the abstraction ant colony clustering algorithm

Applications

Sensitivity Analysis of Main

Findings

Conclusions