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Practical Graph Mining with R

Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a do-it-yourself approach;

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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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Graph Data Mining

applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used;

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Graph Theory

practical problems can be represented by graphs. In this book, the authors present new research on graph theory including the applications of graph;

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R and Data Mining

in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical;

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Advances in Knowledge Discovery and Data Mining

; recommender system; social network and graph mining; data pre-processing and feature selection; representation learning and embedding; mining;

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Advances in Knowledge Discovery and Data Mining

; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining;

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Advances in Knowledge Discovery and Data Mining

; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining;

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Mining Graph Data

This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both;

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Big Data Analytics and Knowledge Discovery

mining, text mining, sequences, graph mining, and parallel processing.;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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A Primer on Process Mining: Practical Skills with Python and Graphviz

to render these graphs intuitively. Overall, it enables the reader to implement process mining techniques on his or her own, independently;

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Advanced Data Mining and Applications

submissions. The papers were organized in topical sections named: Machine Learning; Text Mining; Graph Mining; Predictive Analytics; Recommender;

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R Data Mining

are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a;

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Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery

data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data;

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Data Mining with R

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining;

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Data Mining with R

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining;

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Data Mining

, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data;

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Producing High-Quality Figures Using SAS/Graph(r) and Ods Graphics Procedures

Create Top-Quality Figures Using SAS Producing High-Quality Figures Using SASGRAPH (R) and ODS Graphics Procedures gives statisticians and;

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Web and Big Data

were carefully reviewed and selected from 184 submissions. The papers are organized around the following topics: Graph Mining; Data Mining;

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Web Information Systems Engineering WISE 2020

organized in the following topical sections: Part I: network embedding; graph neural network; social network; graph query; knowledge graph;

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Web Information Systems Engineering WISE 2020

organized in the following topical sections: Part I: network embedding; graph neural network; social network; graph query; knowledge graph;

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Mastering Text Mining with R

skills for building text-mining apps with R with this easy-to-follow guide * Gain in-depth understanding of the text mining process with lucid;

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Web Information Systems Engineering - WISE 2019

linkage and disambiguation; graph learning; knowledge graphs; graph mining; and text mining.;

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Graph-based Natural Language Processing and Information Retrieval

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these;

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