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Karthikeyani Visalakshi, N.
- An Application of PSO-Based Intuitionistic Fuzzy Clustering to Medical Datasets
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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.References
- J.C. Bezdek, R. Ehrlich and W. Full, “FCM: The Fuzzy C-means Clustering Algorithm”, Computers and Geosciences, Vol. 10, No. 2-3, pp. 191-203, 1984.
- J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference Proceedings on Neural Networks, pp. 1942-1948, 1995.
- V. Kumutha and S. Palaniammal, “Improved Fuzzy Clustering Method Based On Intuitionistic Fuzzy Particle Swarm Optimization”, Journal of Theoretical and Applied Information Technology, Vol. 62, No. 1, pp. 8-15, 2014.
- S.J. Nanda and G. Panda, “A Survey on Nature Inspired Metaheuristic Algorithms for Partitional Clustering”, Swarm and Evolutionary Computation, Vol. 16, pp. 1-18, 2014.
- D. Binu, “Cluster Analysis using Optimization Algorithms with Newly Designed Objective Functions”, Expert Systems with Applications, Vol. 42, No. 14, pp. 5848-5859, 2015.
- H. Izakian and A. Abraham, “Fuzzy C-means and Fuzzy Swarm for Fuzzy Clustering Problem”, Expert Systems with Applications, Vol. 38, No. 3, pp. 1835-1838, 2011.
- A.N. Benaichouche, H. Oulhadj and P. Siarry, “Improved Spatial Fuzzy C-means Clustering for Image Segmentation using PSO Initialization, Mahalanobis Distance and Post-Segmentation Correction”, Digital Signal Processing, Vol. 23, No. 5, pp. 1390-1400, 2013.
- Z. Izakian, M.S. Mesgari and A. Abraham, “Automated Clustering of Trajectory Data using a Particle Swarm Optimization”, Computers, Environment and Urban Systems, Vol. 55, pp. 55-65, 2016.
- J.L. Salmeron, S.A. Rahimi, A.M. Navali and A. Sadeghpour, “Medical diagnosis of Rheumatoid Arthritis using data driven PSO-FCM with Scarce Datasets”, Neurocomputing, Vol. 232, pp. 104-112, 2017.
- A. Saxena et al., “A Review of Clustering Techniques and Developments”, Neurocomputing, Vol. 267, pp. 664-681, 2017.
- D. Hein, A. Hentschel, T. Runkler and S. Udluft, “Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies”, Engineering Applications of Artificial Intelligence, Vol. 65, pp. 87-98, 2017.
- J. Valente De Oliveira, A. Szabo and L.N. De Castro, “Particle Swarm Clustering in Clustering Ensembles”, Applied Soft Computing, Vol. 55, pp. 141-153, 2017.
- Marco S. Nobile et al., “Fuzzy Self-Tuning PSO: A Settings-Free Algorithm for Global Optimization”, Swarm and Evolutionary Computation, 2017.
- T.M. Silva Filho, B.A. Pimentel, R.M. Souza and A.L. Oliveira, “Hybrid methods for Fuzzy Clustering based on Fuzzy C-means and Improved Particle Swarm Optimization”, Expert Systems with Applications, Vol. 42, No. 17, pp. 6315-6328, 2015.
- A. Mekhmoukh, and K. Mokrani, “Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) Initialization and Outlier Rejection with Level Set methods for MR Brain Image Segmentation”, Computer Methods and Programs in Biomedicine, Vol. 122, No. 2, pp. 266-281, 2015.
- T. Chaira, “A Novel Intuitionistic Fuzzy C means Clustering Algorithm and its Application to Medical Images”, Applied Soft Computing, Vol. 11, No. 2, pp. 1711-1717, 2011.
- S. Shanthi and V.M. Bhaskaran, “Intuitionistic Fuzzy C-means and Decision Tree Approach for Breast Cancer Detection and Classification”, European Journal of Scientific Research, Vol. 66, No. 3, pp. 345-351, 2011.
- T. Chaira and S. Anand. “A Novel Intuitionistic Fuzzy Approach for Tumour/Hemorrhage Detection in Medical Images”, Journal of Scientific and Industrial Research, Vol. 70, No. 6, pp. 427-434, 2011.
- Z. Xu and J. Wu, “Intuitionistic Fuzzy C-means Clustering Algorithms”, Journal of Systems Engineering and Electronics, Vol. 21, No. 4, pp. 580-590, 2010.
- P. Kaur, A.K. Soni and A. Gosain, “Robust Intuitionistic Fuzzy C-means Clustering for Linearly and Nonlinearly Separable Data”, Proceedings of IEEE International Conference on Image Information Processing, pp. 1-6, 2011.
- R. Bhargava et al., “Rough Intuitionistic Fuzzy C-means Algorithm and A Comparative Analysis”, Proceedings of 6th ACM India Computing Convention, pp. 1-23, 2013.
- P. Balasubramaniam, and V.P. Ananthi, “Segmentation of Nutrient Deficiency in Incomplete Crop Images using Intuitionistic Fuzzy C-means Clustering Algorithm”, Nonlinear Dynamics, Vol. 83, No. 1-2, pp. 849-866, 2016.
- V.P. Ananthi, P. Balasubramaniam, and C.P. Lim, “Segmentation of Gray Scale Image based on Intuitionistic Fuzzy sets Constructed from Several Membership Functions”, Pattern Recognition, Vol. 47, No. 12, pp. 3870-3880, 2014.
- S. Parvathavarthini, N. Karthikeyani, S. Shanthi, and J M Mohan, “Cuckoo-Search based Intuitionistic Fuzzy Clustering Algorithm”, Asian Journal of Research in Social Sciences and Humanities, Vol. 7, No. 2, pp. 289-299, 2017.
- S. Parvathavarthini, N. Karthikeyani, S. Shanthi, and K. Lakshmi, “Crow-Search-Based Intuitionistic Fuzzy C-Means Clustering Algorithm”, Developments and Trends in Intelligent Technologies and Smart Systems, pp. 1- 22, 2017.
- N.K. Visalakshi, S. Parvathavarthini and K. Thangavel, “An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset”, Proceedings of International Conference on Computational Intelligence, Cyber Security and Computational Models, pp. 79-87, 2014.
- L.A. Zadeh, “Fuzzy Sets”, Information and control, Vol. 8, No. 3, pp. 338-353, 1965.
- K.T. Atanassov, “Intuitionistic Fuzzy Sets: Past, Present and Future”, Proceedings of 3rd Conference of the European Society for Fuzzy Logic and Technology, pp. 12-19, 2003.
- I.K. Vlachos and G.D. Sergiadis, “The Role of Entropy in Intuitionistic Fuzzy Contrast Enhancement”, Proceedings of International Fuzzy Systems Association World Congress, pp. 104-113, 2007.
- Russell C. Eberhart, Yuhui Shi and James Kennedy, “Swarm Intelligence”, 1st Edition, Morgan Kaufmann, 2001.
- A. Asuncion and D.J. Newman, “UCI Repository of Machine Learning Databases”, Ph.D. Dissertation, University of California, 2007.
- M. Halkidi, Y. Batistakis and M. Vazirgiannis, “Cluster Validity Methods: part I”, ACM SIGMOD Record, Vol. 31, No. 2, pp. 40-45, 2002.
- J. C. Dunn, “Well-Separated Clusters and Optimal Fuzzy Partitions”, Journal of Cybernetics, Vol. 4, No. 1, pp. 95-104, 1974.
- C.J. Van Rijsbergen, “Information Retrieval”, Ph.D. Dissertation, Department of Computer Science, University of Glasgow, 1979.
- Clustering Categorical Data Using K-Modes Based on Cuckoo Search Optimization Algorithm
Abstract Views :199 |
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
- Z. Huang, “A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining”, Proceedings of Data Mining and Knowledge Discovery, pp. 1-6, 1997.
- Z. Huang, “Extensions to the K-means Algorithm for Clustering Large Data Sets with Categorical Value”, Data Mining and Knowledge Discovery, Vol. 2, No. 3, pp. 283- 304, 1998.
- G. Gan, C. Ma and J. Wu, “Data Clustering: Theory, Algorithms, and Applications”, Society for Industrial and Applied Mathematics, 2007.
- X.S. Yang and S. Deb, “Cuckoo Search via Levy Flights”, Proceedings of IEEE World Congress in Nature and Biologically Inspired Computing, pp. 210-214, 2009.
- X.S. Yang and S. Deb, “Engineering Optimisation by Cuckoo Search”, International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, pp. 330-343, 2010
- Z. Huang and M.K Ng, “A Fuzzy K-Modes Algorithm for Clustering Categorical Data”, IEEE Transactions on Fuzzy Systems, Vol. 7, No. 4, pp. 446-452, 1999.
- M.K. Ng and J.C Wong, “Clustering Categorical Data Sets using Tabu Search Techniques”, Pattern Recognition, Vol. 35, No. 12, pp. 2783-2790, 2002.
- F. Glover and M. Laguna, “Tabu Search”, Kluwer Academic Publishers, 1997.
- G. Gan, Z. Yang and J. Wu, “A Genetic K-Modes Algorithm for Clustering Categorical Data”, Proceedings of International Conference on Advanced Data Mining and Applications, pp. 195-202, 2005
- J.H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”, MIT press, 1992.
- G. Gan, J. Wu and Z. Yang, “A Genetic Fuzzy K-Modes Algorithm for Clustering Categorical Data”, Expert Systems with Applications, Vol. 36, No. 2, pp. 1615-1620, 2009.
- H. Izakian, A. Abraham and V. Snasel, “Clustering Categorical Data using a Swarm-based Method”, Proceedings of World Congress on In Nature and Biologically Inspired Computing, pp. 1720-1724, 2009.
- L. Mei and Z. Xiang-Jun, “A Novel PSO k-Modes Algorithm for Clustering Categorical Data”, Proceedings of Computer, Informatics, Cybernetics and Applications, pp. 1395-1402, 2012
- X. Zhao and M. Lu, “3D Object Retrieval Based on PSO-K-Modes Method”, Multimedia Tools and Applications, Vol. 8, No. 4, pp. 963-970, 2013.
- J. Ji, W. Pang, Y. Zheng, Z. Wang and Z. Ma, “A Novel Artificial Bee Colony based Clustering Algorithm for Categorical Data”, PLOS One, Vol. 10, No. 5, pp. 1-6, 2015.
- G.G. Wang, A.H. Gandomi, X. Zhao and H.C. Chu, “Hybridizing Harmony Search Algorithm with Cuckoo Search for Global Numerical Optimization”, Soft Computing, Vol. 20, No. 1, pp. 273-85, 2016
- L. Yu, Z. Dong, H. Wang and Y. Ding, “The Cuckoo Search Algorithm based on Fuzzy C-Mean Clustering”, Proceedings of 36th Chinese Control Conference, pp. 2691-2696, 2017
- K. Lakshmi, N. Karthikeyani Visalakshi and S. Shanthi. “Cuckoo Search based K-Prototype Clustering Algorithm”, Asian Journal of Research in Social Sciences and Humanities, Vol. 7, No. 2, pp. 300-309, 2017.
- K. Lakshmi, N. Karthikeyani Visalakshi, S. Shanthi and S. Parvathavarthini, “Clustering Mixed Datasets using K-Prototype Algorithm based on Crow-Search Optimization”, Proceedings of Developments and Trends in Intelligent Technologies and Smart Systems, pp. 191-197, 2017.
- F. Van Den Bergh, “An Analysis of Particle Swarm Optimizers (PSO)”, PhD Dissertation, Faculty of Natural and Agricultural Science, University of Pretoria, 2001.
- A. Asuncion and D. Newman, “UCI Machine Learning Repository”, Available at: http://www.ics.uci.edu/~mlearn/ MLRepository.html, Accessed on 2007.
- C.J. Van Rijsbergen, “Information Retrieval”, PhD Dissertation, Department of Computer Science, University of Glasgow, 1979.
- W.M. Rand, “Objective Criteria for the Evaluation of Clustering Methods”, Journal of the American Statistical association, Vol. 66, No. 336, pp. 846-850, 1971.