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From Social Data Mining and Analysis...

Kaya, Mehmet.

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From Social Data Mining and Analysis to Prediction and Community Detection

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	From Social Data Mining and Analysis to Prediction and Community Detection/ edited by Mehmet Kaya, Ozcan Erdogan, Jon Rokne.
其他作者:	Kaya, Mehmet.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	x, 245 p. :ill., digital ;24 cm.
內容註:	Chapter1. An Offline-Online Visual Framework for Clustering Memes in Social Media -- Chapter2. A System for Email Recipient Prediction -- Chapter3. A Credibility Assessment Model for Online Social Network Content -- Chapter4. Web Search Engine based Representation for Arabic Tweets Categorization -- Chapter5. Sentiment Trends and Classifying Stocks using P-Trees -- Chapter6. Mining Community Structure with Node Embeddings -- Chapter7. A LexDFS-based Approach on finding compact communities -- Chapter8. Computational Data Sciences and Regulation of Banking and Financial Services -- Chapter9. Frequent and Non-Frequent Sequential Itemsets Detection.
Contained By:	Springer eBooks
標題:	Data mining. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-51367-6
ISBN:	9783319513676

From Social Data Mining and Analysis to Prediction and Community Detection
From Social Data Mining and Analysis to Prediction and Community Detection[electronic resource] /edited by Mehmet Kaya, Ozcan Erdogan, Jon Rokne. - Cham :Springer International Publishing :2017. - x, 245 p. :ill., digital ;24 cm. - Lecture notes in social networks,2190-5428. - Lecture notes in social networks..

Chapter1. An Offline-Online Visual Framework for Clustering Memes in Social Media -- Chapter2. A System for Email Recipient Prediction -- Chapter3. A Credibility Assessment Model for Online Social Network Content -- Chapter4. Web Search Engine based Representation for Arabic Tweets Categorization -- Chapter5. Sentiment Trends and Classifying Stocks using P-Trees -- Chapter6. Mining Community Structure with Node Embeddings -- Chapter7. A LexDFS-based Approach on finding compact communities -- Chapter8. Computational Data Sciences and Regulation of Banking and Financial Services -- Chapter9. Frequent and Non-Frequent Sequential Itemsets Detection.

This book presents the state-of-the-art in various aspects of analysis and mining of online social networks. Within the broader context of online social networks, it focuses on important and upcoming topics of social network analysis and mining. The book collects chapters that are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2015), which was held in Paris, France in August 2015. All papers have been peer reviewed and checked carefully for overlap with the literature. The book will appeal to students and researchers in social network analysis/mining and machine learning.

ISBN: 9783319513676

Standard No.: 10.1007/978-3-319-51367-6doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

From Social Data Mining and Analysis to Prediction and Community Detection
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505 0 $a Chapter1. An Offline-Online Visual Framework for Clustering Memes in Social Media -- Chapter2. A System for Email Recipient Prediction -- Chapter3. A Credibility Assessment Model for Online Social Network Content -- Chapter4. Web Search Engine based Representation for Arabic Tweets Categorization -- Chapter5. Sentiment Trends and Classifying Stocks using P-Trees -- Chapter6. Mining Community Structure with Node Embeddings -- Chapter7. A LexDFS-based Approach on finding compact communities -- Chapter8. Computational Data Sciences and Regulation of Banking and Financial Services -- Chapter9. Frequent and Non-Frequent Sequential Itemsets Detection.
520 $a This book presents the state-of-the-art in various aspects of analysis and mining of online social networks. Within the broader context of online social networks, it focuses on important and upcoming topics of social network analysis and mining. The book collects chapters that are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2015), which was held in Paris, France in August 2015. All papers have been peer reviewed and checked carefully for overlap with the literature. The book will appeal to students and researchers in social network analysis/mining and machine learning.
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