Journal ArticleDOI
Interactive Mining of Strong Friends from Social Networks and Its Applications in E-Commerce
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This study integrates data mining with social computing to form a social network mining algorithm, which helps the individual distinguish these strong friends from a large number of friends in a specific portion of the social networks in which he or she is interested.Abstract:
Social networks are generally made of individuals who are linked by some types of interdependencies such as friendship. Most individuals in social networks have many linkages in terms of friends, connections, and/or followers. Among these linkages, some of them are stronger than others. For instance, some friends may be acquaintances of an individual, whereas others may be friends who care about him or her (e.g., who frequently post on his or her wall). In this study, we integrate data mining with social computing to form a social network mining algorithm, which helps the individual distinguish these strong friends from a large number of friends in a specific portion of the social networks in which he or she is interested. Moreover, our mining algorithm allows the individual to interactively change his or her mining parameters. Furthermore, we discuss applications of our social mining algorithm to organizational computing and e-commerceread more
Citations
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Journal ArticleDOI
Models and methods in social network analysis
TL;DR: Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s.
Journal ArticleDOI
Sports Data Mining: Predicting Results for the College Football Games
Carson K. Leung,Kyle W. Joseph +1 more
TL;DR: A sports data mining approach is presented, which helps discover interesting knowledge and predict outcomes of sports games such as college football, and makes predictions based on a combination of four different measures on the historical results of the games.
Journal ArticleDOI
Parallel social network mining for interesting 'following' patterns
TL;DR: A space‐efficient bitwise data structure for capturing interdependency among social entities; a time-efficient data mining algorithm that makes the best use of the proposed data structure; and another time‐efficient datamining algorithm for concurrent computation and discovery of groups of frequently followed social entities in parallel so as to handle high volumes of social network data.
Proceedings ArticleDOI
A machine learning approach for stock price prediction
TL;DR: This paper applies structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure and uses an SSVM to predict positive or negative movement in their stock prices.
Book ChapterDOI
Big Data Analytics of Social Network Data: Who Cares Most About You on Facebook?
TL;DR: This book chapter presents big data management and analytics techniques on social network data that help users discover friends or connections who cares most about them on social networking sites such as Facebook.
References
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Book
Social Network Analysis: Methods and Applications
TL;DR: This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of Dyadic and Triadic Interaction Models, which describes the relationships between actor and group measures and the structure of networks.
Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Journal ArticleDOI
Social Network Analysis: Methods and Applications.
TL;DR: This work characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links that connect them.
Journal ArticleDOI
Mining frequent patterns without candidate generation
Jiawei Han,Jian Pei,Yiwen Yin +2 more
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
Journal ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.