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Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

This book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A;

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Recent Advances in Swarm Intelligence and Evolutionary Computation

and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout;

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Evolutionary Machine Learning Techniques

, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons;

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Evolutionary Machine Learning Techniques

, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the;

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Graph-theoretic Techniques For Web Content Mining

using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety;

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Computational Methods of Feature Selection

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields;

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Topology Control for Mobile Ad hoc Networks

algorithm for clustering in MANETs. In our algorithm we propose a heuristic based approach by considering degrees of the nodes during node selection;

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Supervised and Unsupervised Learning for Data Science

of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models;

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Data Clustering

communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area;

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Improved Scheduling Algorithm Using Dynamic Tree Construction for Wireless Sensor Networks

algorithm based on Distance and Residual Energy for Wireless Sensor Networks (DECSA) which describes scheduling based on distance and energy, and;

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Rough-Fuzzy Pattern Recognition

recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm and applications. Special;

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Apache Mahout Clustering Designs

Explore clustering algorithms used with Apache Mahout About This Book * Use Mahout for clustering datasets and gain useful insights;

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Advances in Knowledge Discovery and Data Mining

; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms;

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Advances in Knowledge Discovery and Data Mining

; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms;

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Data Mining for Bioinformatics

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a;

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Data Mining for Bioinformatics

Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a;

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Data Clustering in C++

algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design;

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Data Clustering in C++

algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design;

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Hierarchical Feature Selection for Knowledge Discovery

state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable;

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Experiment and Evaluation in Information Retrieval Models

Solutions to the Problems in Advanced IR Concepts, including Feature Selection for Document Ranking, web page classification and recommendation;

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Automatic construction of labeled clusters of named entities for IR

algorithm presented by Benjamin Van Durme and M.Pasca (2008) to assign a label to the clusters. This automatic lableing task is able to assign a;

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Artificial Intelligence and Data Mining in Healthcare

compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science;

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Document Processing Using Machine Learning

* Machine learning algorithms for document analysis * Extreme learning machines and their applications * Mathematical foundation for Web text;

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Machine Learning with Python Cookbook Practical Solutions from Preprocessing to Deep Learning

such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe;

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Fuzzy Approach to Hierarchical Clustering

search of Tolerance coefficient for Definite hierarchical agglomerative clustering method and to find Partition optimality coefficient by means;

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Computational Intelligence And Feature Selection

theories Computational Intelligence and Feature Selection is an ideal resource for advanced undergraduates, postgraduates, researchers, and;

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Building Search Applications

algorithm for a query, and suggest spelling corrections. Find the names of people, places, and other entities in text using LingPipe and Gate;

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