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: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering; Laith Mohammad Qasim Abualigah Book 2019 Springer Nature

[復(fù)制鏈接]
查看: 23942|回復(fù): 38
樓主
發(fā)表于 2025-3-21 19:49:59 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering
編輯Laith Mohammad Qasim Abualigah
視頻videohttp://file.papertrans.cn/342/341564/341564.mp4
概述Presents a new method for solving the text document clustering problem and demonstrates that it can outperform other comparable methods.Covers the main text clustering preprocessing steps and the meta
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering;  Laith Mohammad Qasim Abualigah Book 2019 Springer Nature
描述.This book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A new feature selection method based on a particle swarm optimization algorithm with a novel weighting scheme is proposed, as well as a detailed dimension reduction technique, in order to obtain a new subset of more informative features with low-dimensional space. This new subset is subsequently used to improve the performance of the text clustering (TC) algorithm and reduce its computation time. The k-mean clustering algorithm is used to evaluate the effectiveness of the obtained subsets. (ii) Four krill herd algorithms (KHAs), namely, the (a) basic KHA, (b) modified KHA, (c) hybrid KHA, and (d) multi-objective hybrid KHA, are proposed to solve the TC problem; each algorithm represents an incremental improvement on its predecessor. For the evaluation process, seven benchmark text datasets are used with different characterizations and complexities..Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where all documents in the same cluster are similar. The findings presented her
出版日期Book 2019
關(guān)鍵詞Krill Herd Algorithm; KHA; Text Document Clustering; Dimension Reduction Techniques; Clustering Algorith
版次1
doihttps://doi.org/10.1007/978-3-030-10674-4
isbn_ebook978-3-030-10674-4Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering影響因子(影響力)




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering影響因子(影響力)學(xué)科排名




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering網(wǎng)絡(luò)公開(kāi)度




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering被引頻次




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering被引頻次學(xué)科排名




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering年度引用




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering年度引用學(xué)科排名




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering讀者反饋




書目名稱Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering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 21:33:38 | 只看該作者
第141564主題貼--第2樓 (沙發(fā))
板凳
發(fā)表于 2025-3-22 00:49:47 | 只看該作者
板凳
地板
發(fā)表于 2025-3-22 07:30:08 | 只看該作者
第4樓
5#
發(fā)表于 2025-3-22 08:42:55 | 只看該作者
5樓
6#
發(fā)表于 2025-3-22 13:15:48 | 只看該作者
6樓
7#
發(fā)表于 2025-3-22 19:16:13 | 只看該作者
7樓
8#
發(fā)表于 2025-3-22 23:22:08 | 只看該作者
8樓
9#
發(fā)表于 2025-3-23 02:09:40 | 只看該作者
9樓
10#
發(fā)表于 2025-3-23 07:07:55 | 只看該作者
10樓
 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, 2025-10-5 17:35
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
光山县| 浪卡子县| 荥经县| 清河县| 沧州市| 雅安市| 浦东新区| 福海县| 策勒县| 安庆市| 桑植县| 乌兰县| 无棣县| 仪陇县| 大关县| 安康市| 准格尔旗| 南京市| 施秉县| 云南省| 庆城县| 石屏县| 莱阳市| 虎林市| 航空| 东城区| 柘城县| 长春市| 泽库县| 西和县| 民县| 乌兰县| 黎川县| 东城区| 曲阜市| 灯塔市| 保山市| 吉木萨尔县| 万盛区| 平谷区| 绍兴县|