Andere Kunden interessierten sich auch für
![Recent Advances in Ensembles for Feature Selection]( "Recent Advances in Ensembles for Feature Selection")
Recent Advances in Ensembles for Feature Selection74,99 €![Recent Advances in Ensembles for Feature Selection]( "Recent Advances in Ensembles for Feature Selection")
Recent Advances in Ensembles for Feature Selection74,99 €![Intelligent Text Categorization and Clustering]( "Intelligent Text Categorization and Clustering")
Intelligent Text Categorization and Clustering74,99 €![Advances in Feature Selection for Data and Pattern Recognition]( "Advances in Feature Selection for Data and Pattern Recognition")
Advances in Feature Selection for Data and Pattern Recognition74,99 €![Methodologies and Intelligent Systems for Technology Enhanced Learning, 8th International Conference]( "Methodologies and Intelligent Systems for Technology Enhanced Learning, 8th International Conference")
Methodologies and Intelligent Systems for Technology Enhanced Learning, 8th International Conference111,99 €![Automatically Ordering Events and Times in Text]( "Automatically Ordering Events and Times in Text")
Automatically Ordering Events and Times in Text74,99 €![Intelligent Text Categorization and Clustering]( "Intelligent Text Categorization and Clustering")
Intelligent Text Categorization and Clustering74,99 €
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.
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.
"The book is well written, with high-quality tables and graphs. Each chapter ends with a collection of references, including the most recent work in the area. The book should be very useful for scholars who want to study the general field of text document clustering. It is also a good reference for those who work in text document clustering and use genetic algorithms." (Xiannong Meng, ComputingReviews, May 10, 2019)