TY - BOOK ID - 46172355 TI - Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering PY - 2019 SN - 3030106748 303010673X PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Engineering. KW - Artificial intelligence. KW - Computational Intelligence. KW - Artificial Intelligence. KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Document clustering. KW - Clustering, Document KW - Cluster analysis KW - File organization (Computer science) KW - Computational intelligence. KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing UR - https://www.unicat.be/uniCat?func=search&query=sysid:46172355 AB - 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. ER -