<p><p>This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc.</p><p></p><p>The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Unders
Cluster Analysis and Genetic Algorithms
✍ Scribed by Petr D., Pavel P.
- Tongue
- English
- Leaves
- 9
- Category
- Library
No coin nor oath required. For personal study only.
✦ Synopsis
Paper, 9 p.
The paper deals with the cluster analysis and genetic algorithms and describes their basis. The application of genetic algorithms is focused on a cluster analysis as an optimization task. The case studies present the way of solution of two and three dimensional cluster analysis in MATLAB program with use of the Genetic Algorithm and Direct Search Toolbox. The way of its possible use in business is mentioned as well.
✦ Subjects
Информатика и вычислительная техника;Искусственный интеллект;Эволюционные алгоритмы
📜 SIMILAR VOLUMES
Department of Computing Imperial College of Science, Technology and Medicine<br/>University of London, London SW7 2AZ.<br/>A dissertation submitted in partial fulfilment of the requirements<br/>for the degree of Doctor of Philosophy of the University of London.<div class="bb-sep"></div>Abstract<br/>
<p><p>This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization,
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, fr
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, fr