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A Clustering-Based Approach for Discovering Places in Trajectories

This work is a clustering point of view about how deduce interesting places considering the points and its speed in a trajectory context;

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Discovering and Analyzing Scientific Communities

This book presents a model and a tool for discovering and analyzing scientific communities. Several approaches have been proposed to;

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A Graph Theoretic Approach to Heterogeneous Data Clustering

-clustering. First, we propose a semantic-based hierarchical image clustering framework based on multi-user feedback. By treating each user as an;

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Apache Mahout Clustering Designs

modelling using Dirichlet process. Finally, you will learn how to build and deploy a model for production use. Style and approach This book;

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Sequence Analysis and Related Approaches

others original propositions for measuring the precarity of work trajectories, Markov-based methods for clustering sequences, fuzzy and;

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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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Clustering

two above mentioned methods. Clustering: A Data Recovery Approach, Second Edition presents a unified modeling approach for the most;

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Clustering

two above mentioned methods. Clustering: A Data Recovery Approach, Second Edition presents a unified modeling approach for the most;

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Clustering

This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to;

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Partitional Clustering via Nonsmooth Optimization

implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering;

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Partitional Clustering via Nonsmooth Optimization

implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering;

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Social Clustering

-based family, community, and regional and national prosperity. Aspiration for common objective; consensus on the most important priorities;

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Data Clustering

used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic;

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Partitional Clustering Algorithms

, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.;

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Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems

databases. The coverage includes an extended Delaunay triangulation-based approach for fingerprint biometrics, involving a classification based on;

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Improved Scheduling Algorithm Using Dynamic Tree Construction for Wireless Sensor Networks

of deploying WSN is to observe a particular site for monitoring physical parameters like temperature, light, pressure, humidity or the occurrence of a;

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Constrained Clustering

discusses an interactive visual clustering approach, a distance metric learning approach, existential constraints, and automatically generated;

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Lunar and Interplanetary Trajectories

This book provides readers with a clear description of the types of lunar and interplanetary trajectories, and how they influence;

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Projection Based Clustering through Self Organization and Swarm Intelligence

This open access book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data;

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Time Series Clustering and Classification

methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features;

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Time Series Clustering and Classification

classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students;

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Pro Linux High Availability Clustering

. In this book four common cases will be explained: Configuring Apache for high availability Creating an Open Source SAN based on DRBD;

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Modern Algorithms of Cluster Analysis

described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants;

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Mobile, Wireless and Sensor Networks

sensor networks (WSNs) is to prolong the battery life of the individual sensors and the network lifetime. For a successful clustering approach;

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Mobile, Wireless and Sensor Networks

sensor networks (WSNs) is to prolong the battery life of the individual sensors and the network lifetime. For a successful clustering approach;

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Temporal Data Mining via Unsupervised Ensemble Learning

be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining;

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Graph Classification And Clustering Based On Vector Space Embedding

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It;

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