Unsupervised Classification
Autor Sanghamitra Bandyopadhyay, Sriparna Sahaen Limba Engleză Hardback – 12 dec 2012
This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.
The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
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Specificații
ISBN-13: 9783642324505
ISBN-10: 3642324509
Pagini: 280
Ilustrații: XVIII, 262 p.
Dimensiuni: 160 x 241 x 20 mm
Greutate: 0.54 kg
Ediția:2013
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642324509
Pagini: 280
Ilustrații: XVIII, 262 p.
Dimensiuni: 160 x 241 x 20 mm
Greutate: 0.54 kg
Ediția:2013
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
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Public țintă
GraduateCuprins
Chap. 1 Introduction.- Chap. 2 Some Single- and Multiobjective Optimization Techniques.- Chap. 3 SimilarityMeasures.- Chap. 4 Clustering Algorithms.- Chap. 5 Point Symmetry Based Distance Measures and their Applications to Clustering.- Chap. 6 A Validity Index Based on Symmetry: Application to Satellite Image Segmentation.- Chap. 7 Symmetry Based Automatic Clustering.- Chap. 8 Some Line Symmetry Distance Based Clustering Techniques.- Chap. 9 Use of Multiobjective Optimization for Data Clustering.- References.- Index.
Recenzii
From the reviews:
“The book focuses on emerging metaheuristic approaches to unsupervised classification, with an emphasis on a symmetry-based definition of similarity. … I found this book very appealing. I also thought of it as very valuable for my preoccupations towards the real-world application of unsupervised classification to medical imaging. I thus believe that, when reading this book, junior as well as experienced researchers will find many new challenging theoretical and practical ideas.” (Catalin Stoean, zbMATH, Vol. 1276, 2014)
“The book views clustering as a (multiobjective) optimization problem and tackles it with metaheuristics algorithms. More interestingly, the authors of this book propose the exploitation of the concepts of point and line symmetry to define new distances to be used in clustering techniques. … researchers in the field will surely appreciate it as a good reference on the use of the symmetry notion in clustering.” (Nicola Di Mauro, Computing Reviews, July, 2013)
“The book focuses on emerging metaheuristic approaches to unsupervised classification, with an emphasis on a symmetry-based definition of similarity. … I found this book very appealing. I also thought of it as very valuable for my preoccupations towards the real-world application of unsupervised classification to medical imaging. I thus believe that, when reading this book, junior as well as experienced researchers will find many new challenging theoretical and practical ideas.” (Catalin Stoean, zbMATH, Vol. 1276, 2014)
“The book views clustering as a (multiobjective) optimization problem and tackles it with metaheuristics algorithms. More interestingly, the authors of this book propose the exploitation of the concepts of point and line symmetry to define new distances to be used in clustering techniques. … researchers in the field will surely appreciate it as a good reference on the use of the symmetry notion in clustering.” (Nicola Di Mauro, Computing Reviews, July, 2013)
Notă biografică
Dr. Anirban Mukhopadhyay is currently a Professor of the Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal. He obtained his Ph.D. in Computer Science and Engineering from Jadavpur University, Kolkata, India in 2009. He received Erasmus Mundus fellowship in 2009 to carry out post-doctoral research at University of Heidelberg and DKFZ, Heidelberg, Germany during 2009-10. Dr. Mukhopadhyay also worked as visiting professor/scientist at University of Nice Sophia-Antipolis, France, University of Goettingen, Germany (with DAAD scholarship), Colorado State University, USA (with Fulbright-Nehru Fellowship), University of Greifswald, Germany, and University of Lodz, Poland (with Erasmus+ fellowship). He received IEI Young Engineers Award (2013-14) in Computer Engineering Discipline, and INAE Young Engineer Award (2014). He has coauthored two books and about 200 research papers in various International Journals and Conferences. Dr. Mukhopadhyaydelivered invited lectures and served in the Technical Program Committees in many national and international conferences in India and abroad. He is a senior member of IEEE and ACM, and a Fellow of West Bengal Academy of Science and Technology. Dr. Mukhopadhyay received the prestigious Sikhsha Ratna award from Govt. of West Bengal in 2020. He has served as a secretary of IEEE Computational intelligence Society, Kolkata Chapter and currently acts as the Vice-Chair of its Executive Committee. He has successfully guided eleven Ph.D. scholars. His research interests include soft and evolutionary computing, data mining and machine learning, multiobjective optimization, bioinformatics and crowdsourcing.
Dr. Sumanta Ray is currently an associate professor in the Department of Computer Science and Engineering at Ghani Khan Choudhury Institute of Engineering & Technology (GKCIET), Malda, India. He earned his PhD in Computer Science and Engineering from Jadavpur University in 2017. Dr. Ray received an ERCIM (European Research Consortium for Informatics and Mathematics) grant to pursue postdoctoral research at CENTRUM WISKUNDE and INFORMATICA (CWI), the Netherlands, from 2019 to 2020. He was a recipient of the DST Inspire Fellowship from the Government of India and also received the Senior Research Fellowship from CSIR (Council of Scientific and Industrial Research), MHRD, Government of India. Previously, Dr. Ray served as a Junior Professor at Universität Bielefeld, Bielefeld, Germany, and as an Assistant Professor at the Department of Computer Science and Engineering, Aliah University, Kolkata. He is currently on lien from Aliah University. Dr. Ray has co-authored more than 40 research papers in various journals and conferences. His research interests include bioinformatics, soft and evolutionary computing, multiobjective optimization, deep learning, and pattern recognition.
Dr. Ujjwal Maulik is a professor in the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India since 2004. He was also the former head of the same Department. He also held the position of the principal in charge and the head of the Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, India. Dr. Maulik has worked in many universities and research laboratories around the world as visiting professor/scientist including Los Alamos National Laboratory, USA; University of New South Wales, Australia; University of Texas at Arlington, USA; University of Maryland at Baltimore County, USA; Fraunhofer Institute for Autonomous Intelligent Systems, St. Augustin, Germany; Tsinghua University, China; Sapienza University, Rome, Italy; University of Heidelberg, Germany; German Cancer Research Center (DKFZ), Germany; Grenoble INP, France; University of Warsaw; University of Padova, Italy; Corvinus University, Budapest; University of Ljubljana, Slovenia; InternationalCenter for Theoretical Physics (ICTP), Trieste, Italy. H
Textul de pe ultima copertă
Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature.
This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.
The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.
The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
Caracteristici
Describes both well-established and metaheuristic clustering techniques Offers a theoretical analysis of symmetry-based clustering techniques Includes extensive real-world applications in remote-sensing satellite imaging, MR brain imaging, bioinformatics, and face detection Includes supplementary material: sn.pub/extras