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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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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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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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Advanced Data Mining and Applications

algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics;

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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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Data Mining

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

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Advanced Data Mining and Applications

This book constitutes the proceedings of the 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, held;

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Web Information Systems Engineering WISE 2020

mining; security and privacy; recommender system; database system and workflow; and data mining and applications;

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Web Information Systems Engineering WISE 2020

mining; security and privacy; recommender system; database system and workflow; and data mining and applications;

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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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Data Analytics

, data mining and knowledge discovery, graph data and knowledge graphs, intelligent data analysis, approximate and flexible querying, data;

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Web and Big Data

Analytics; Data and Information Quality; Data Mining and Application; Graph Data and Social Networks; Information Extraction and Retrieval;

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Web and Big Data

Analytics; Data and Information Quality; Data Mining and Application; Graph Data and Social Networks; Information Extraction and Retrieval;

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Advances in Knowledge Discovery and Data Mining

methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data;

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Advances in Knowledge Discovery and Data Mining

methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data;

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Web and Big Data

submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender;

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Web and Big Data

submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender;

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Big Data Analytics and Knowledge Discovery

. The papers are organized in topical sections on Mining Big Data, Applications of Big Data Mining, Big Data Indexing and Searching, Big Data;

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Big Data Analytics and Knowledge Discovery

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

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Web Information Systems Engineering - WISE 2019

topical sections: blockchain and crowdsourcing; machine learning; deep learning; recommender systems, data mining; web-based applications; entity;

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Matrix Methods in Data Mining and Pattern Recognition

computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra;

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