A New Algorithm for Clustering Based on Particle Swarm Optimization and K-means

Dong, Jing; Qi, Minyong

doi:10.1109/aici.2009.394

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…In other words, it is difficult to effectively target the customers using the clusters obtained from K ‐means. The results of our article also buttress the finding of recent studies that the cluster outputs from standard commercial packages that use a K ‐means approach are significantly less robust than previously assumed (Wang, Obremski, Alidaee, & Kochenberger, 2008; Dong & Qi, 2009; Na, Xumin, & Yong, 2010).…”

Section: Introductionsupporting

confidence: 85%

Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Balakrishnan¹,

Kumar²,

Han³

2011

Decision Sciences

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Cluster-based segmentation usually involves two sets of variables: (i) the needs-based variables (referred to as the bases variables), which are used in developing the original segments to identify the value, and (ii) the classification or background variables, which are used to profile or target the customers. The managers' goal is to utilize these two sets of variables in the most efficient manner. Pragmatic managerial interests recognize the underlying need to start shifting from methodologies that obtain highly precise value-based segments but may be of limited practical use as they provide less targetable segments. Consequently, the imperative is to shift toward newer segmentation approaches that provide greater focus on targetable segments while maintaining homogeneity. This requires dual objective segmentation, which is a combinatorially difficult problem. Hence, we propose and examine a new evolutionary methodology based on genetic algorithms to address this problem. We show, based on a large-scale Monte Carlo simulation and a case study, that the proposed approach consistently outperforms the existing methods for a wide variety of problem instances. We are able to obtain statistically significant and managerially important improvements in targetability with little diminution in the identifiability of value-based segments. Moreover, the proposed methodology provides a set of good solutions, unlike existing methodologies that provide a single solution. We also show how these good solutions can be used to plot an efficient Pareto frontier. Finally, we present useful insights that would help managers in * We thank Prof. Gary Erickson, Bob Jacobson, V. "Senu" Srinivasan, and the marketing seminar participants at the University of Washington, Hebrew University, The University of Sydney, and the NASMEI conference for their helpful comments. P. V. (Sundar) Balakrishnan thanks Prof. Paul E. Green and Gary L. Lilien for their encouragement and support. Subodha Kumar thanks the Mays Business School, Texas A&M University, for partially supporting this research with a Summer Research Performance Recognition Grant. We especially thank Prof. Brusco for his generous assistance in providing us with his simulated annealing code, which made the direct comparison possible. † Corresponding author. 832Dual Objective Segmentation to Improve Targetability implementing the proposed solution approach effectively.

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Section: Introductionsupporting

confidence: 85%

Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Balakrishnan¹,

Kumar²,

Han³

2011

Decision Sciences

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“…It is a process of grouping objects into clusters such that the objects from the same clusters are similar and objects from different clusters are dissimilar. The clustering algorithms usually can be classified into the following four categories: (a) partitioning clustering; (b) density-based and grid-based clustering; (c) hierarchical clustering; (d) other clustering [3], [14], [15]. K-means algorithm is one of the most popular partitioning clustering algorithms used for clustering large scale datasets with the advantages of simplicity and high efficiency.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Class Assignment Algorithms for Performance Measure of Clustering Algorithms

Zhang

Xue

Wang

2012

2012 Eighth International Conference on Computational Intelligence and Security

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To measure the performance or validity of clustering algorithms, several evaluation values, such as successful rate, successful number and full successful rate are defined. In order to ensure each cluster to at least contain one vector data, and to maximize several proposed evaluation values, two class assignment algorithms are designed. To testify their performance, we employ them to the k-means clustering algorithms.

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“…Izakian et al (2009) proposed a hybrid fuzzy clustering method based on FCM and fuzzy PSO (FPSO), which make use of the merits of both algorithms. Dong and Qi (2009b) proposed a new hybrid algorithm based on particle swarm optimization and K-means algorithm. The next solution of the problem is generated by the better one of PSO and K-means but not PSO itself.…”

Section: Hybrid Evolutionary Algorithms For Clusteringmentioning

confidence: 99%

A survey: hybrid evolutionary algorithms for cluster analysis

Hasan¹,

Ramakrishnan²

2011

Artif Intell Rev

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Clustering is a popular data analysis and data mining technique. It is the unsupervised classification of patterns into groups. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as slowness of the convergence, sensitive to initial value and preset classed in large scale data set etc. and they still require much investigation to improve performance and efficiency. Over the last decade, clustering with ant-based and swarm-based algorithms are emerging as an alternative to more traditional clustering techniques. Many complex optimization problems still exist, and it is often very difficult to obtain the desired result with one of these algorithms alone. Thus, robust and flexible techniques of optimization are needed to generate good results for clustering data. Some algorithms that imitate certain natural principles, known as evolutionary algorithms have been used in a wide variety of real-world applications. Recently, much research has been proposed using hybrid evolutionary algorithms to solve the clustering problem. This paper provides a survey of hybrid evolutionary algorithms for cluster analysis.

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A New Algorithm for Clustering Based on Particle Swarm Optimization and K-means

Cited by 8 publications

References 9 publications

Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Class Assignment Algorithms for Performance Measure of Clustering Algorithms

A survey: hybrid evolutionary algorithms for cluster analysis

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