找回密碼
 To register

QQ登錄

只需一步，快速開(kāi)始

掃一掃，訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Computational Intelligence in Data Mining - Volume 2; Proceedings of the I Lakhmi C. Jain,Himansu Sekhar Behera,Durga Prasad Conference pr

[復(fù)制鏈接]
查看: 34292|回復(fù): 61
樓主
發(fā)表于 2025-3-21 16:37:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computational Intelligence in Data Mining - Volume 2
副標(biāo)題Proceedings of the I
編輯Lakhmi C. Jain,Himansu Sekhar Behera,Durga Prasad
視頻videohttp://file.papertrans.cn/233/232482/232482.mp4
概述Presents latest research findings in data mining.Entails thought-provoking developments to help research students.Discusses most recent cutting edge scientific technologies in computing.Includes suppl
叢書(shū)名稱Smart Innovation, Systems and Technologies
圖書(shū)封面Titlebook: Computational Intelligence in Data Mining - Volume 2; Proceedings of the I Lakhmi C. Jain,Himansu Sekhar Behera,Durga Prasad Conference pr
描述The contributed volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.
出版日期Conference proceedings 2015
關(guān)鍵詞Advance Computing Methods; Big Data Analysis; CIDM; CIDM 2014; CIDM 2014 Proceedings; CIDM Proceedings; Co
版次1
doihttps://doi.org/10.1007/978-81-322-2208-8
isbn_softcover978-81-322-3561-3
isbn_ebook978-81-322-2208-8Series ISSN 2190-3018 Series E-ISSN 2190-3026
issn_series 2190-3018
copyrightSpringer India 2015
The information of publication is updating

書(shū)目名稱Computational Intelligence in Data Mining - Volume 2影響因子(影響力)




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2影響因子(影響力)學(xué)科排名




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2被引頻次




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2被引頻次學(xué)科排名




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2年度引用




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2年度引用學(xué)科排名




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2讀者反饋




書(shū)目名稱Computational Intelligence in Data Mining - Volume 2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(méi)有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 21:52:11 | 只看該作者
Strategic Analysis: Strategy Modeling, of metrics for measuring the understand ability of conceptual data model for data warehouses. The statistical and machine learning methods are used to predict effect of structural metrics, on understand ability, efficiency and effectiveness of Data warehouse Multidimensional (MD) conceptual model.
板凳
發(fā)表于 2025-3-22 00:38:29 | 只看該作者
Strategic Planning for The Family Businesshas a strong global search capability is used for dimensionality optimization. Weighted aggregation method is employed as multi-objective functions. The proposed system has the high intrusion detection accuracy of 97.54?% with a detection time is 0.20?s.
地板
發(fā)表于 2025-3-22 06:47:06 | 只看該作者
5#
發(fā)表于 2025-3-22 11:27:00 | 只看該作者
6#
發(fā)表于 2025-3-22 16:33:11 | 只看該作者
,A Study of Interestingness Measures for Knowledge Discovery in Databases—A Genetic Approach,user to select appropriate measure in a particular application domain. The main contribution of the paper is to compare these interestingness measures on diverse datasets by using genetic algorithm and select the best one according to the situation.
7#
發(fā)表于 2025-3-22 21:01:38 | 只看該作者
Quality Assessment of Data Using Statistical and Machine Learning Methods, of metrics for measuring the understand ability of conceptual data model for data warehouses. The statistical and machine learning methods are used to predict effect of structural metrics, on understand ability, efficiency and effectiveness of Data warehouse Multidimensional (MD) conceptual model.
8#
發(fā)表于 2025-3-23 01:07:35 | 只看該作者
9#
發(fā)表于 2025-3-23 02:40:02 | 只看該作者
10#
發(fā)表于 2025-3-23 07:49:54 | 只看該作者
 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(ài)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, 2025-10-19 01:15
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
安义县| 庆元县| 新巴尔虎右旗| 银川市| 永年县| 澄江县| 商丘市| 苗栗市| 武胜县| 富川| 莱西市| 扎赉特旗| 南宫市| 峨眉山市| 凤山县| 湖北省| 崇义县| 平潭县| 永年县| 三河市| 丹棱县| 河间市| 那坡县| 无锡市| 双流县| 抚顺市| 屏东县| 桦南县| 连州市| 通化市| 耒阳市| 油尖旺区| 汤阴县| 麦盖提县| 修武县| 田林县| 永安市| 东丰县| 和平区| 张家港市| 广水市|