| 书目名称 | Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases |
| 编辑 | Ashish Ghosh,Satchidananda Dehuri,Susmita Ghosh |
| 视频video | |
| 概述 | Assembles high quality original contributions that reflect and advance the state-of-the art in the area of Multi-objective Evolutionary Algorithms for Data Mining and Knowledge Discovery.Emphasizes on |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | .Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM...The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.. |
| 出版日期 | Book 20081st edition |
| 关键词 | Knowledge Discovery Form Databases; algorithm; algorithms; calculus; classification; clustering; data mini |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-77467-9 |
| isbn_softcover | 978-3-642-09615-0 |
| isbn_ebook | 978-3-540-77467-9Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer-Verlag Berlin Heidelberg 2008 |
发表于 2025-3-21 17:24:25
