東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

From Social Data Mining and Analysis...

Kaya, Mehmet.

Linked to FindBook

Google Book

Amazon

博客來

From Social Data Mining and Analysis to Prediction and Community Detection

Record Type:	Electronic resources : Monograph/item
Title/Author:	From Social Data Mining and Analysis to Prediction and Community Detection/ edited by Mehmet Kaya, Ozcan Erdogan, Jon Rokne.
other author:	Kaya, Mehmet.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	x, 245 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Data mining. -
Online resource:	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
LDR:02402nmm a2200337 a 4500 001 2092569
003 DE-He213
005 20170321120910.0
006 m d
007 cr nn 008maaau
008 171117s2017 gw s 0 eng d
020 $a 9783319513676 $q (electronic bk.)
020 $a 9783319513669 $q (paper)
024 7 $a 10.1007/978-3-319-51367-6 $2 doi
035 $a 978-3-319-51367-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b F931 2017
245 0 0 $a From Social Data Mining and Analysis to Prediction and Community Detection $h [electronic resource] / $c edited by Mehmet Kaya, Ozcan Erdogan, Jon Rokne.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a x, 245 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in social networks, $x 2190-5428
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.
650 0 $a Data mining. $3 562972
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Applications of Graph Theory and Complex Networks. $3 3134760
700 1 $a Kaya, Mehmet. $3 3226537
700 1 $a Erdogan, Ozcan. $3 3226538
700 1 $a Rokne, Jon. $3 2108722
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Lecture notes in social networks. $3 2058983
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-51367-6
950 $a Computer Science (Springer-11645)