ISBN: 9783642100710

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been… More...

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable. Books > Computer Science Soft cover, Springer Shop<

ISBN: 9783642100710

Paperback, [PU: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG], In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is… More...

Paperback, [PU: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG], In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions., Artificial Intelligence<

2010, ISBN: 3642100716

Hardcover

Softcover reprint of hardcover 1st ed. 2009 Kartoniert / Broschiert Künstliche Intelligenz, algorithms; data mining; evolution; heuristics; kernel; knowledge; learning; modeling; neural… More...

Softcover reprint of hardcover 1st ed. 2009 Kartoniert / Broschiert Künstliche Intelligenz, algorithms; data mining; evolution; heuristics; kernel; knowledge; learning; modeling; neural network; optimization, mit Schutzumschlag neu, [PU:Springer Berlin Heidelberg; Springer Berlin]<

2009, ISBN: 3642100716

Hardcover

Metaheuristic Clustering ab 160.49 € als Taschenbuch: Softcover reprint of hardcover 1st ed. 2009. Aus dem Bereich: Bücher, Ratgeber, Computer & Internet, Medien > Bücher, Springer Berlin… More...

Metaheuristic Clustering ab 160.49 € als Taschenbuch: Softcover reprint of hardcover 1st ed. 2009. Aus dem Bereich: Bücher, Ratgeber, Computer & Internet, Medien > Bücher, Springer Berlin Heidelberg<