عنوان

Feature selection and enhanced krill herd algorithm for text document clustering /

شابک

3030106748

شابک

3030106756

شابک

9783030106744

شابک

9783030106751

شابک اشتباه

303010673X

شابک اشتباه

9783030106737

عنوان اصلي

Feature selection and enhanced krill herd algorithm for text document clustering /

نام عام مواد

[Book]

نام نخستين پديدآور

Laith Mohammad Qasim Abualigah.

محل نشرو پخش و غیره

Cham, Switzerland :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

[2019]

نام خاص و کميت اثر

1 online resource

عنوان فروست

Studies in computational intelligence,

مشخصه جلد

Volume 816

شاپا ي ISSN فروست

1860-949X ;

متن يادداشت

Includes bibliographical references.

متن يادداشت

Chapter 1. Introduction -- Chapter 2. Krill Herd Algorithm -- Chapter 3. Literature Review -- Chapter 4. Proposed Methodology -- Chapter 5. Experimental Results -- Chapter 6. Conclusion and Future Work -- References -- List Of Publications.

بدون عنوان

0

متن يادداشت

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 here confirm that the proposed methods and algorithms delivered the best results in comparison with other, similar methods to be found in the literature.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783030106744

عنوان

Feature selection and enhanced krill herd algorithm for text document clustering.

شماره استاندارد بين المللي کتاب و موسيقي

9783030106737

موضوع مستند نشده

Document clustering.

موضوع مستند نشده

Document clustering.

موضوع مستند نشده

COM004000

موضوع مستند نشده

UYQ

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Abualigah, Laith Mohammad Qasim

تاريخ عمليات

20200823082448.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y