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710	2		_aSpringerLink (Online service) _9106996
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008			181218s2019 gw a o |||| 0|eng d
020			_a9783030106744
024	7		_a10.1007/978-3-030-10674-4 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA278.55 _b2019 EB
100	1		_aAbualigah, Laith Mohammad Qasim _eautor _9671486
245	1	0	_aFeature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering _cby Laith Mohammad Qasim Abualigah.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XXVII, 165 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aStudies in Computational Intelligence _x1860-949X _v816
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aChapter 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.
520	3		_aThis 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.
988			_aPrimersemestre_2019_Robotics
650		7	_2embne _9669583 _aAnálisis Cluster
776	0	8	_iPrinted edition: _z9783030106737
776	0	8	_iPrinted edition: _z9783030106751
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-10674-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI