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A Graph Theoretic Approach to Heterogeneous Data Clustering

present a novel graph theoretic approach to perform pairwise heterogeneous data co-clustering. We then propose Isoperimetric Co-clustering;

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Graph-theoretic Techniques For Web Content Mining

of graph distance - a relatively new approach for determining graph similarity - the authors show how well-known algorithms, such as k-means;

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Biological Network Analysis

Interaction Networks (PPIN) and Human Brain Connectomes Includes a discussion of various graph theoretic and data analytics approaches

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Data Clustering

of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social;

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Heterogeneous Graph Representation Learning and Applications

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to;

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Quantitative Graph Theory

) Graph measures and statistics Information-theoretic methods to analyze graphs quantitatively (e.g., entropy) Through its broad;

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Quantitative Graph Theory

) Graph measures and statistics Information-theoretic methods to analyze graphs quantitatively (e.g., entropy) Through its broad;

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Managing and Mining Graph Data

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety;

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Managing and Mining Graph Data

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety;

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Advances in Knowledge Discovery and Data Mining

; behavioral data mining; graph clustering and community detection; dimensionality reduction.;

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Advances in Knowledge Discovery and Data Mining

; behavioral data mining; graph clustering and community detection; dimensionality reduction.;

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Modern Algorithms of Cluster Analysis

longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing;

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Apache Spark Graph Processing

processing from loading and transforming graph data to * Improve the scalability of your graphs with a variety of real-world applications with;

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Group-Theoretic Algorithms and Graph Isomorphism

Group-Theoretic Algorithms and Graph Isomorphism is een boek van C.M. Hoffmann;

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Spectral Clustering And Biclustering

graph theory and statistics by giving answers to the demanding questions which arise when statisticians are confronted with large weighted;

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Clustering in Bioinformatics and Drug Discovery

information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and;

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Clustering in Bioinformatics and Drug Discovery

information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and;

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Database and Expert Systems Applications

were carefully reviewed and selected from 166 submissions. The papers discuss a range of topics including: Semantic Web and Semantics; Graph;

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Database and Expert Systems Applications

were carefully reviewed and selected from 166 submissions. The papers discuss a range of topics including: Semantic Web and Semantics; Graph;

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Graph Theory Applications

, matching, digraphs, networks, matrices of a graph, graph theoretic algorithms, and matroids. These concepts are then applied in the second part to;

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Graph-Powered Machine Learning

* Three end-to-end applications * Graphs in big data platforms * Data source modeling * Natural language processing;

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Tensor Computation for Data Analysis

Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss;

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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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Graph-Based Social Media Analysis

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction;

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Adaptive Resonance Theory in Social Media Data Clustering

heterogeneous data? How to understand a user's interests by learning from online posts and behaviors? How to create a personalized search engine by;

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Structural, Syntactic, and Statistical Pattern Recognition

clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding.;

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