Modified ACS Centroid Memory for Data Clustering

Ayad Mohammed Jabbar,Rafid Sagban,Ku Ruhana Ku-Mahamud

doi:10.3844/jcssp.2019.1439.1449

Abstract

Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm’s memory records previous search regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant’s movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.

Highlights

Clustering is an unsupervised learning technique that groups data without any prior information
This study proposes a new pheromone memory approach that can retain the history of previously recorded information to be used in the learning process, in which such information is a learning tool employed in the algorithm
The data sets are from different problems, such as disease that includes Breast Cancer (BC), Breast Tissue (BT), Hepatitis (Hp) and E. coli (Ec); ascertaining the survival of patients (Haberman (Hb)); image analysis of the ionosphere (Io); metal signals in mining operations

Summary

Introduction

Clustering is an unsupervised learning technique that groups data without any prior information This approach is an undirected learning technique that can be performed without the need for labeled data. The stochastic approach involves the problem of minimizing or maximizing an objective function to achieve a clustering solution.

Results

Discussion

Conclusion