找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Data Mining and Computational Intelligence; Abraham Kandel,Mark Last,Horst Bunke Book 2001 Physica-Verlag Heidelberg 2001 computational in

[復(fù)制鏈接]
查看: 40437|回復(fù): 49
樓主
發(fā)表于 2025-3-21 19:46:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Mining and Computational Intelligence
編輯Abraham Kandel,Mark Last,Horst Bunke
視頻videohttp://file.papertrans.cn/263/262926/262926.mp4
概述Comprehensive coverage of recent advances in the application of soft computing and fuzzy logic data mining.Also useful as a reference book in data mining, machine learning, fuzzy logic, and artificial
叢書名稱Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Data Mining and Computational Intelligence;  Abraham Kandel,Mark Last,Horst Bunke Book 2001 Physica-Verlag Heidelberg 2001 computational in
描述Many business decisions are made in the absence of complete information about the decision consequences. Credit lines are approved without knowing the future behavior of the customers; stocks are bought and sold without knowing their future prices; parts are manufactured without knowing all the factors affecting their final quality; etc. All these cases can be categorized as decision making under uncertainty. Decision makers (human or automated) can handle uncertainty in different ways. Deferring the decision due to the lack of sufficient information may not be an option, especially in real-time systems. Sometimes expert rules, based on experience and intuition, are used. Decision tree is a popular form of representing a set of mutually exclusive rules. An example of a two-branch tree is: if a credit applicant is a student, approve; otherwise, decline. Expert rules are usually based on some hidden assumptions, which are trying to predict the decision consequences. A hidden assumption of the last rule set is: a student will be a profitable customer. Since the direct predictions of the future may not be accurate, a decision maker can consider using some information from the past. The
出版日期Book 2001
關(guān)鍵詞computational intelligence; data mining; database; database management; fuzzy; fuzzy logic; intelligence; k
版次1
doihttps://doi.org/10.1007/978-3-7908-1825-3
isbn_softcover978-3-7908-2484-1
isbn_ebook978-3-7908-1825-3Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightPhysica-Verlag Heidelberg 2001
The information of publication is updating

書目名稱Data Mining and Computational Intelligence影響因子(影響力)




書目名稱Data Mining and Computational Intelligence影響因子(影響力)學(xué)科排名




書目名稱Data Mining and Computational Intelligence網(wǎng)絡(luò)公開度




書目名稱Data Mining and Computational Intelligence網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Mining and Computational Intelligence被引頻次




書目名稱Data Mining and Computational Intelligence被引頻次學(xué)科排名




書目名稱Data Mining and Computational Intelligence年度引用




書目名稱Data Mining and Computational Intelligence年度引用學(xué)科排名




書目名稱Data Mining and Computational Intelligence讀者反饋




書目名稱Data Mining and Computational Intelligence讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 21:29:25 | 只看該作者
板凳
發(fā)表于 2025-3-22 02:59:06 | 只看該作者
地板
發(fā)表于 2025-3-22 06:58:52 | 只看該作者
Mining Fuzzy Association Rules in a Database Containing Relational and Transactional Data,echnique for the mining of such rules in databases containing both types of data. This technique, which we call Fuzzy Miner, performs its tasks by the use of fuzzy logic, a set of transformation functions, and by residual analysis. With the transformation functions, new attributes and new item types
5#
發(fā)表于 2025-3-22 10:37:58 | 只看該作者
Fuzzy Linguistic Summaries via Association Rules,ata mining. Links between our approach to linguistic summaries and the well-known technique of association rules is shown. The generation of linguistic summaries is implemented by using the authors’ FQUERY for Access package.
6#
發(fā)表于 2025-3-22 12:53:19 | 只看該作者
The Fuzzy-ROSA Method: A Statistically Motivated Fuzzy Approach for Data-Based Generation of Small for data-based rule generation has been demonstrated impressively in numerous real-world tasks. However, there are still difficulties in generating small interpretable rule bases efficiently, especially for applications with many input variables. The Fuzzy-ROSA method presented here was developed to
7#
發(fā)表于 2025-3-22 17:41:55 | 只看該作者
8#
發(fā)表于 2025-3-23 00:20:46 | 只看該作者
9#
發(fā)表于 2025-3-23 01:39:40 | 只看該作者
Mining a Growing Feature Map by Data Skeleton Modelling, knowledge discovery applications. In this article, we present a further extension to the GSOM in which the cluster identification process can be automated. The self-generating ability of the GSOM is used to identify the paths along which the GSOM grew, and these paths are used to develop a skeleton
10#
發(fā)表于 2025-3-23 07:01:12 | 只看該作者
Some Practical Applications of Soft Computing and Data Mining,application areas, however, data is more complicated: real-life data is often obtained as an image from a camera rather than a few measurements. Furthermore, this image can also change dynamically. In this paper, we present several examples of how soft computing is related to mining such data.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-11 09:19
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
晋州市| 商南县| 屏东县| 闽侯县| 清水县| 桑日县| 松阳县| 淄博市| 宣恩县| 渭南市| 台南市| 榕江县| 云南省| 虹口区| 德江县| 永新县| 济源市| 武城县| 阳东县| 三原县| 桃江县| 邓州市| 抚顺市| 水富县| 东至县| 无极县| 改则县| 密山市| 墨玉县| 嘉禾县| 黔西| 新津县| 斗六市| 临朐县| 天祝| 丰城市| 虎林市| 沾化县| 信宜市| 淳化县| 册亨县|