| 书目名称 | Evolutionary Computation in Data Mining |
| 编辑 | Ashish Ghosh,Lakhmi C. Jain |
| 视频video | |
| 概述 | State of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms.Demonstrates how the different tools of evolutionary computation can be used for solving real-life prob |
| 丛书名称 | Studies in Fuzziness and Soft Computing |
| 图书封面 |  |
| 描述 | Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search |
| 出版日期 | Book 2005 |
| 关键词 | Data mining; Evolutionary Computation; Knowledge Discovery in Databases; Multi-Agent Data mining; algori |
| 版次 | 1 |
| doi | https://doi.org/10.1007/3-540-32358-9 |
| isbn_softcover | 978-3-642-42195-2 |
| isbn_ebook | 978-3-540-32358-7Series ISSN 1434-9922 Series E-ISSN 1860-0808 |
| issn_series | 1434-9922 |
| copyright | Springer-Verlag Berlin Heidelberg 2005 |
发表于 2025-3-21 19:32:45
