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Parvathavarthini, S.
- An Application of PSO-Based Intuitionistic Fuzzy Clustering to Medical Datasets
Abstract Views :217 |
PDF Views:4
Authors
Affiliations
1 School of Computer Technology and Applications, Kongu Engineering College, IN
2 Department of Computer Science, NKR Government Arts College for Women, IN
1 School of Computer Technology and Applications, Kongu Engineering College, IN
2 Department of Computer Science, NKR Government Arts College for Women, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 1 (2017), Pagination: 1531-1538Abstract
Clustering is the process of splitting data into several groups based on the characteristics of data. Fuzzy clustering assigns a data object to various clusters based on different membership values. In medical field, the diagnosis of the disease has to be done without faults and in an earlier time without any delay. So, there is a need to represent imprecise nature of the data. To represent vague data in a clear manner, Intuitionistic fuzzy set introduces a parameter called hesitancy degree. In case of Intuitionistic fuzzy clustering, this indicates that the user is not aware whether the object belongs to or not belongs to a cluster. In such a case, hesitancy can very well represent the inherent noise in the data or the ignorance of the user that is given by the state ‘may be’. All clustering algorithms choose the initial seed in a random fashion. But, this creates a serious impact on the convergence of the algorithm and the clustering algorithms tend to fall into local minima. This work utilizes Intuitionistic fuzzy Particle Swarm Optimization to initialize the centroids for the Intuitionistic fuzzy clustering algorithm. The algorithm is executed over medical datasets from UCI repository and the results indicate that optimal clusters are achieved. The proposed method performs well when compared with IFCM and FCM-PSO.Keywords
Clustering, Intuitionistic Fuzzy Set, Particle Swarm Optimization, Inertia Weight, Lambda Value.
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- Clustering Categorical Data Using K-Modes Based on Cuckoo Search Optimization Algorithm
Abstract Views :193 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Applications, Kongu Engineering College, IN
2 Department of Computer Science, NKR Government Arts College for Women, IN
3 Department of Computer Technology, Kongu Engineering College, IN
1 Department of Computer Applications, Kongu Engineering College, IN
2 Department of Computer Science, NKR Government Arts College for Women, IN
3 Department of Computer Technology, Kongu Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 1 (2017), Pagination: 1561-1566Abstract
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
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