| 书目名称 | Inhibitory Rules in Data Analysis |
| 副标题 | A Rough Set Approach |
| 编辑 | Pawel Delimata,Mikhail Ju. Moshkov,Zbigniew Suraj |
| 视频video | |
| 概述 | The state of the art of inhibitory rules in data analysis and rough sets |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | This monograph is devoted to theoretical and experimental study of inhibitory decision and association rules. Inhibitory rules contain on the right-hand side a relation of the kind “attribut = value”. The use of inhibitory rules instead of deterministic (standard) ones allows us to describe more completely infor- tion encoded in decision or information systems and to design classi?ers of high quality. The mostimportantfeatureofthis monographis thatit includesanadvanced mathematical analysis of problems on inhibitory rules. We consider algorithms for construction of inhibitory rules, bounds on minimal complexity of inhibitory rules, and algorithms for construction of the set of all minimal inhibitory rules. We also discuss results of experiments with standard and lazy classi?ers based on inhibitory rules. These results show that inhibitory decision and association rules can be used in data mining and knowledge discovery both for knowledge representation and for prediction. Inhibitory rules can be also used under the analysis and design of concurrent systems. The results obtained in the monograph can be useful for researchers in such areas as machine learning, data mining and knowled |
| 出版日期 | Book 2009 |
| 关键词 | Computational Intelligence; Data Analysis; Extension; Inhibitory Rules; Rough Sets; algorithm; algorithms; |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-85638-2 |
| isbn_softcover | 978-3-642-09927-4 |
| isbn_ebook | 978-3-540-85638-2Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer-Verlag Berlin Heidelberg 2009 |
发表于 2025-3-21 18:09:56
