Metaheuristic Clustering:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2009
|
| Schriftenreihe: | Studies in Computational Intelligence
178 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHN01 FHR01 Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9783540939641 |
| DOI: | 10.1007/978-3-540-93964-1 |
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Datensatz im Suchindex
| _version_ | 1804152238797488128 |
|---|---|
| any_adam_object | |
| author | Das, Swagatam |
| author_GND | (DE-588)123881315 (DE-588)1063311365 |
| author_facet | Das, Swagatam |
| author_role | aut |
| author_sort | Das, Swagatam |
| author_variant | s d sd |
| building | Verbundindex |
| bvnumber | BV041889673 |
| classification_rvk | QH 234 |
| collection | ZDB-2-ENG |
| contents | Metaheuristic Pattern Clustering – An Overview -- Differential Evolution Algorithm: Foundations and Perspectives -- Modeling and Analysis of the Population-Dynamics of Differential Evolution Algorithm -- Automatic Hard Clustering Using Improved Differential Evolution Algorithm -- Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm -- Clustering Using Multi-objective Differential Evolution Algorithms -- Conclusions and Future Research |
| ctrlnum | (OCoLC)698912582 (DE-599)BVBBV041889673 |
| dewey-full | 519 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519 |
| dewey-search | 519 |
| dewey-sort | 3519 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1007/978-3-540-93964-1 |
| format | Electronic eBook |
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| id | DE-604.BV041889673 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:07:32Z |
| institution | BVB |
| isbn | 9783540939641 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027333627 |
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| series | Studies in Computational Intelligence |
| series2 | Studies in Computational Intelligence |
| spelling | Das, Swagatam Verfasser aut Metaheuristic Clustering by Swagatam Das, Ajith Abraham, Amit Konar Berlin, Heidelberg Springer Berlin Heidelberg 2009 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Studies in Computational Intelligence 178 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 Metaheuristic Pattern Clustering – An Overview -- Differential Evolution Algorithm: Foundations and Perspectives -- Modeling and Analysis of the Population-Dynamics of Differential Evolution Algorithm -- Automatic Hard Clustering Using Improved Differential Evolution Algorithm -- Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm -- Clustering Using Multi-objective Differential Evolution Algorithms -- Conclusions and Future Research Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Metaheuristik (DE-588)4820176-5 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 s Metaheuristik (DE-588)4820176-5 s Evolutionärer Algorithmus (DE-588)4366912-8 s DE-604 Abraham, Ajith 1968- Sonstige (DE-588)123881315 oth Konar, Amit 1963- Sonstige (DE-588)1063311365 oth Erscheint auch als Druckausgabe 978-3-540-92172-1 Studies in Computational Intelligence 178 (DE-604)BV020822171 178 https://doi.org/10.1007/978-3-540-93964-1 Verlag Volltext |
| spellingShingle | Das, Swagatam Metaheuristic Clustering Studies in Computational Intelligence Metaheuristic Pattern Clustering – An Overview -- Differential Evolution Algorithm: Foundations and Perspectives -- Modeling and Analysis of the Population-Dynamics of Differential Evolution Algorithm -- Automatic Hard Clustering Using Improved Differential Evolution Algorithm -- Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm -- Clustering Using Multi-objective Differential Evolution Algorithms -- Conclusions and Future Research Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Evolutionärer Algorithmus (DE-588)4366912-8 gnd Metaheuristik (DE-588)4820176-5 gnd Cluster-Analyse (DE-588)4070044-6 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4820176-5 (DE-588)4070044-6 |
| title | Metaheuristic Clustering |
| title_auth | Metaheuristic Clustering |
| title_exact_search | Metaheuristic Clustering |
| title_full | Metaheuristic Clustering by Swagatam Das, Ajith Abraham, Amit Konar |
| title_fullStr | Metaheuristic Clustering by Swagatam Das, Ajith Abraham, Amit Konar |
| title_full_unstemmed | Metaheuristic Clustering by Swagatam Das, Ajith Abraham, Amit Konar |
| title_short | Metaheuristic Clustering |
| title_sort | metaheuristic clustering |
| topic | Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Evolutionärer Algorithmus (DE-588)4366912-8 gnd Metaheuristik (DE-588)4820176-5 gnd Cluster-Analyse (DE-588)4070044-6 gnd |
| topic_facet | Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Evolutionärer Algorithmus Metaheuristik Cluster-Analyse |
| url | https://doi.org/10.1007/978-3-540-93964-1 |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT dasswagatam metaheuristicclustering AT abrahamajith metaheuristicclustering AT konaramit metaheuristicclustering |