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Dr. Anirban Mukhopadhyay
University of Kalyani, India
Multiobjective Genetic Algorithms for Clustering: Method and Applications
Clustering is an important data mining technique where a set of patterns, usually vectors in multidimensional space, are grouped into clusters based on some similarity or dissimilarity criteria. Clustering techniques aim to find a suitable grouping of the input dataset so that some criteria, such as compactness, separation, and connectivity are optimized. A straightforward way to pose clustering as an optimization problem is to optimize some cluster validity index that reflects the goodness of the clustering solutions. All possible partitionings of the dataset and the corresponding values of the validity index define the complete search space. Traditional partitional clustering techniques, such as K-means and fuzzy C-means, employ greedy search techniques over the search space to optimize the compactness of the clusters. These algorithms often get stuck at some local optima depending on the choice of the initial cluster centers. Moreover, they optimize a single cluster validity index (compactness in this case), and therefore do not cover different characteristics of the datasets. To overcome the problem of local optima, some evolutionary global optimization tools such as Genetic Algorithms (GAs) have been widely used to reach the global optimum value of the chosen validity measure. Conventional GA-based clustering techniques use some validity measure as the fitness value. However, no single validity measure works equally well for different kinds of datasets. Thus it is natural to simultaneously optimize multiple such measures for capturing different characteristics of the data. Simultaneous optimization of multiple objectives provides improved robustness to different data properties. Hence it is useful to utilize multiobjective GAs (MOGAs) for clustering. In this talk, I will first describe some preliminaries of multiobjective optimization and Pareto optimality. Subsequently, I will describe a multiobjective GA-based clustering algorithm. Finally I will demonstrate some applications of multiobjective clustering technique in remote sensing and bioinformatics.
Dr. Anirban Mukhopadhyay is currently an Associate Professor of the Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India. He was the Head of the same department during September 2012 – September 2014. He did his B.E. from National Institute of Technology, Durgapur, India, in 2002 and M.E. from Jadavpur University, Kolkata, India, in 2004, respectively, both in Computer Science and Engineering. He obtained his Ph.D. in Computer Science from Jadavpur University in 2009. Dr. Mukhopadhyay is the recipient of the University Gold Medal and Amitava Dey Memorial Gold Medal from Jadavpur University in 2004 for ranking first class first in M.E. He received Erasmus Mundus fellowship in 2009 to carry out post-doctoral research at University of Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany during 2009-10. Dr. Mukhopadhyay also visited I3S laboratory, University of Nice Sophia-Antipolis, Nice, France in 2011 as a Visiting Professor, and University of Goettingen, Germany, as a Visiting Scientist with DAAD scholarship in 2013. He has received Institution of Engineers, India (IEI) Young Engineers Award 2013-14 in Computer Engineering Discipline, and Indian National Academy of Engineering (INAE) Young Engineer Award 2014. Dr. Mukhopadhyay has recently received LOKMAT National Education Leadership Award as the Best Teacher in Computer Science and Engineering in 2015. He has coauthored one book and over 120 research papers in various reputed International Journals and Conferences. He is a senior member of Institute of Electrical and Electronics Engineers (IEEE), USA, and member of Association for Computing Machinery (ACM), USA, and International Association of Engineers (IAENG), Hong Kong. Dr. Mukhopadhyay is currently the Secretary of IEEE Computational Intelligence Society, Kolkata Chapter. His research interests include soft and evolutionary computing, data mining, multiobjective optimization, pattern recognition, bioinformatics, and optical networks.
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