ICTACT Journal on Soft Computing

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Clustering Categorical Data Using K-Modes Based on Cuckoo Search Optimization Algorithm


Affiliations
1 Department of Computer Applications, Kongu Engineering College, India
2 Department of Computer Science, NKR Government Arts College for Women, India
3 Department of Computer Technology, Kongu Engineering College, India
     

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Cluster analysis is the unsupervised learning technique that finds the interesting patterns in the data objects without knowing class labels. Most of the real world dataset consists of categorical data. For example, social media analysis may have the categorical data like the gender as male or female. The k-modes clustering algorithm is the most widely used to group the categorical data, because it is easy to implement and efficient to handle the large amount of data. However, due to its random selection of initial centroids, it provides the local optimum solution. There are number of optimization algorithms are developed to obtain global optimum solution. Cuckoo Search algorithm is the population based metaheuristic optimization algorithms to provide the global optimum solution. Methods: In this paper, k-modes clustering algorithm is combined with Cuckoo Search algorithm to obtain the global optimum solution. Results: Experiments are conducted with benchmark datasets and the results are compared with k-modes and Particle Swarm Optimization with k-modes to prove the efficiency of the proposed algorithm.

Keywords

Cluster Analysis, k-Modes, Cuckoo Search Optimization, Local Optima, Initial Centroids.
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  • Clustering Categorical Data Using K-Modes Based on Cuckoo Search Optimization Algorithm

Abstract Views: 260  |  PDF Views: 4

Authors

K. Lakshmi
Department of Computer Applications, Kongu Engineering College, India
N. Karthikeyani Visalakshi
Department of Computer Science, NKR Government Arts College for Women, India
S. Shanthi
Department of Computer Applications, Kongu Engineering College, India
S. Parvathavarthini
Department of Computer Technology, Kongu Engineering College, India

Abstract


Cluster analysis is the unsupervised learning technique that finds the interesting patterns in the data objects without knowing class labels. Most of the real world dataset consists of categorical data. For example, social media analysis may have the categorical data like the gender as male or female. The k-modes clustering algorithm is the most widely used to group the categorical data, because it is easy to implement and efficient to handle the large amount of data. However, due to its random selection of initial centroids, it provides the local optimum solution. There are number of optimization algorithms are developed to obtain global optimum solution. Cuckoo Search algorithm is the population based metaheuristic optimization algorithms to provide the global optimum solution. Methods: In this paper, k-modes clustering algorithm is combined with Cuckoo Search algorithm to obtain the global optimum solution. Results: Experiments are conducted with benchmark datasets and the results are compared with k-modes and Particle Swarm Optimization with k-modes to prove the efficiency of the proposed algorithm.

Keywords


Cluster Analysis, k-Modes, Cuckoo Search Optimization, Local Optima, Initial Centroids.

References