Applying artificial immune system and ant algorithm in air-conditioner market segmentation

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Abstract

Clustering method is critical to market segmentation. In this paper, we proposed the immunity-based ant clustering algorithm, which integrates two search algorithms, the ant algorithm and the artificial immune system. Ant algorithm, a novel meta-heuristic approach for solving hard combinatorial optimization problems, is utilized to generate good solutions to the clustering problems. Then, the artificial immune system is adopted to search for optimization of clustering problems. Our proposed method is implemented to a real-world clustering problem of air-conditioner market segmentation in 3C chain store. Hypothesis tests are conducted to test the significance among our proposed method and other known clustering methods. As a result, IACA has the best clustering performance.

Introduction

Recently, data mining has attracted a lot of attention in the information industry and academic research due to the wide availability of large amounts of data and the imminent need for turning such data into useful information and knowledge. The gained information and knowledge can be used for applications in business management, production and operation management, forecasting, marketing analysis and science exploration. In the domain of data mining and knowledge discovery, the common issues include association rule mining, classification, prediction and clustering analysis. With the trend of data enormously increasing, how to deal with these huge data for quickly data mining and knowledge discovery becomes one of the most important issues. Data clustering groups the collected data with similar characteristics into the same cluster and analyzes the relationship among these objects or data. Data clustering is not only a single data mining method but also a pre-process of data mining, knowledge discovery and data collection.

Ant algorithm is a heuristic method proposed to solve optimization problems. In a recent research of clustering problem, Ant system-based clustering algorithm, ASCA (Kuo, Wang, Hu, & Chou, 2005), applied the ant algorithm to merge data into the same cluster if they are within the distance of some specified standard deviation from the centroid. However, the objects near the edge of each cluster may result in dissimilarity with higher total within cluster variance (TWCV) than the objects gathered around the centroid. In this situation, it is more rational to group these data into another independent cluster which reduces the TWCV of both clusters. In this paper, we proposed the immunity-based ant clustering algorithm, IACA, which integrates the ant algorithm and the artificial immune system to resolve such situation and search for optimization of clustering problems.

Section snippets

Clustering analysis

Clustering is the process of grouping a set of abstract or physical objects into classes composed of similar objects. It divides data into several groups. The purpose of clustering analysis is to find the difference among each groups and the similarity in the same group. A group is also called a cluster. The cluster is also a collection of data objects similar to one another in the same cluster and dissimilar to others in other clusters.

Unlike classification, clustering does not know the

Definitions

The following definitions are adopted from Kuo et al. (2005). for the further development of our proposed clustering algorithms:

  • Let E = {O1, O2,  On} be the data set with n objects, where O indicates the objects (or data, items) collected from the database. And each object has k attributes (dimensions), where k > 0. The data format is shown as in Table 3.1.

  • n: the number of objects.

  • nc: the number of clusters.

  • T: the set composed of the used objects.

  • Tk: the set T named k.

  • D(i,j): the Euclidean distance

Case study

In order to further examine the proposed algorithm, a real-world case data is applied. In this case, we investigate questionnaires of the customer satisfaction and perception to a large 3C appliance chain store. The proposed clustering method is implemented to group customers into different segments for further customer relation management.

Conclusion

In this paper, we proposed the immunity-based ant clustering algorithm, IACA. It consists of two types of search algorithms, the ant algorithms and the artificial immune system. Ant algorithm is utilized to generate the initial solution. Then, the artificial immune system is adopted to search for optimization of clustering problems. In order to strengthen the demonstration of the proposed algorithm, our proposed algorithm is applied to real-world data for air-conditioner market segmentation.

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