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"Clustering is a diverse topic, and the underlying algorithms depend greatly on the data domain and problem scenario. This book focuses on three primary aspects of data clustering: the core methods such as probabilistic, density-based, grid-based, and spectral clustering etc; different problem domains and scenarios such as multimedia, text, biological, categorical, network, and uncertain data as well as data streams; and different detailed insights from the clustering process because of the subjectivity of the clustering process, and the many different ways in which the same data set can be clustered"--

Document clustering. --- Cluster analysis. --- Data mining. --- Machine theory. --- File organization (computer science). --- Business & economics --- Computers --- Statistics. --- Database management --- Cluster analysis --- Data mining --- Document clustering --- File organization (Computer science) --- Machine theory --- 681.3*I2 --- Abstract automata --- Abstract machines --- Automata --- Mathematical machine theory --- Algorithms --- Logic, Symbolic and mathematical --- Recursive functions --- Robotics --- File management (Computer science) --- File systems (Computer science) --- Organization, File (Computer science) --- Electronic data processing --- Clustering, Document --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI

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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.

Engineering. --- Artificial intelligence. --- Computational Intelligence. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Document clustering. --- Clustering, Document --- Cluster analysis --- File organization (Computer science) --- Computational intelligence. --- Intelligence, Computational --- Artificial intelligence --- Soft computing

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Automatic Text Categorization and Clustering are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. Well known applications are spam filtering and web search, but a large number of everyday uses exist (intelligent web search, data mining, law enforcement, etc.) Currently, researchers are employing many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing. This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering. In the following, we give a brief introduction of the chapters that are included in this book.

Text processing (Computer science) --- Document clustering --- Cluster analysis --- Artificial intelligence --- Computer Science --- Civil Engineering --- Applied Mathematics --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Computer programs --- Computational intelligence. --- Natural language processing (Computer science) --- NLP (Computer science) --- Intelligence, Computational --- Engineering. --- Artificial intelligence. --- Text processing (Computer science). --- Computational linguistics. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Computational Linguistics. --- Document Preparation and Text Processing. --- Language Translation and Linguistics. --- Soft computing --- Electronic data processing --- Human-computer interaction --- Semantic computing --- Natural language processing (Computer science). --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Natural Language Processing (NLP). --- Engineering --- Engineering analysis --- Mathematical analysis --- Automatic language processing --- Language and languages --- Language data processing --- Linguistics --- Natural language processing (Linguistics) --- Applied linguistics --- Cross-language information retrieval --- Mathematical linguistics --- Multilingual computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematics --- Data processing