UNSUPERVISED DATA AND HISTOGRAM CLUSTERING USING INCLINED PLANES SYSTEM OPTIMIZATION ALGORITHM
DOI:
https://doi.org/10.5566/ias.v33.p65-74Keywords:
Davies Bouldin index, histogram, image processing, inclined planes system, optimization, soft computing, unsupervised clusteringAbstract
Within the last decades, clustering has gained significant recognition as one of the data mining methods, especially in the relatively new field of medical engineering for diagnosing cancer. Clustering is used as a database to automatically group items with similar characteristics. Researchers aim to introduce a novel and powerful algorithm known as Inclined Planes system Optimization (IPO), with capacity to overcome clustering problems. The proposed method identifies each agent used in the algorithm to indicate the centroids of the clusters and automatically select the number of centroids in each time interval (unsupervised clustering). The evaluation method for clustering is based on the Davies Bouldin index (DBi) to show cluster validity. Researchers compare known algorithm on series of data bases from various studies to demonstrate the power and capability of the proposed method. These datasets are popular for pattern recognition with diversity in space dimension. Method performance was tested on standard images as a dataset. Study results show significant method advantage over other algorithms.
References
Bandyopadhyay, S., & Maulik, U. (2002). Genetic clustering for automatic evolution of clusters and application to image classification. IEEE pattern recognition, 35, 1197-1208
Chou, C., & et al. (2004). A new cluster validity measure and its application to image compression. Pattern Anal, 205-220.
Davies, D., & Bouldin, D. (2000). A cluster separation measure. IEEE Trans. Pattern Anal Machine Intell, 224-227.
Dembele, D. (2008). Multi-objective optimization for clustering 3-way gene expression data. Advances in Data Analysis and Classification, 211-225.
Dimitriadou, E., Dolnicar, S., & Weingassel, A. (2002). An Examination of Indexes for Detemining the number of Clusters in Binary Data Sets. Psychometrika, 137-160.
Dorigo, M. (1992). Optimization, Learning and Netural Algorithms. Ph.D. Thesis,. Politecnio di Malano, Italy.
Farmer, J., Packard, N., & Perelson, A. (1986). The immune system, adaptation, and machine learning. Physica D archive, 2(13).
Formato, R. (2007). Central Force Optimization: A new metaheuristic with applications in applied electromagnetics. Progress in Electromagnetics Research, 425-491.
Garcia-Escudero, L., & et al. (2010). A review of robust clustering methods. Advances in Data Analysis and Classification, 89-109.
Geem, Z., Kim, J., & Loganathan, G. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 60-68.
Hashimoto , W., Nakamura, T., & Miyamoto, S. (2009). Comparison and Evaluation of Different Cluster Validity Measures Including Their Kernelization. Journal of Advanced Computational Intelligence and Intelligent Informatics, 13(3).
Jackson, G., & et al. (2009). Unsupervised Data clustering and Image Segmentation using Natural Computing Techniques. IEEE international Conference on systems USA.
Jain, A., & et al. (1999). Data clustering, a review. ACM Computer Surveys. ACM Press.
Katari, V., & Satapathy, S. (2007). Hybridized Improved Genetic Algorithm with Variable Length Chromosome for Image Clustering. IJCSNS International Journal of Computer Science and Network Security, 7.
Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948.
Krikpatrick, S., & et al. (1983). Optimization by Simulated Annealing. Science, 671-680.
Maulik, U., & Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Journal of the Pattern Recognition, 33(9), 1455-1465.
Mozaffari, M., Abdi, H., & Zahiri, H. (2012). Inclined Planes system Optimization. Information Science elsevier journal (Submited).
Omran, G., Engelbrecht, A., & Salman, A. (2005, novamber). Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification. Proceedings of world academy of science, engineering and technology, 9.
Omran, M., & Salman , A. (2005). Dynammic clustering using particle swarm optimization with application in unsupervised image classification. Fifth World Enformatika Conference (ICCI).
Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorith. Information Sciences, 2232-2248.
Sang C, S. (2012). Practical Application of DATA MINING. USA: Jones & Bartlett 978-443-5000.
Tang, K., & et al. (1996). Genetic algorithms and their applications. IEEE Signal Processing Magazine, 22-37.
Tseng, L., & Yang, S. (2001). A genetic approach to the automatic clustering problem. Journal of the Pattern Recognition, 34, 415-424.
Xu, R., & Wunsch II, D. (2005, May). Survey of Clustering Algorithms. IEEE Trans. On Networks, 16(3), 645-678.
Xu, R., & Wunsch II, D. (2008). Clustering. Wiley.
Yamamoto , M. (2012, August). Clustering of functional data in a low-dimensional subspace. Advances in Data Analysis and Classification.
Zahiri, S. (2010). Swarm Intelligence and Fuzzy Systems. USA: Nova Science.
