Knowledge Discovery from Data (TKDD)


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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 5 Issue 1, December 2010

ACM TKDD Special Issue on Knowledge Discovery for Web Intelligence
Ning Zhong, Gregory Piatetsky-Shapiro, Yiyu Yao, Philip S. Yu
Article No.: 1
DOI: 10.1145/1870096.1870097

A Combination Approach to Web User Profiling
Jie Tang, Limin Yao, Duo Zhang, Jing Zhang
Article No.: 2
DOI: 10.1145/1870096.1870098

In this article, we study the problem of Web user profiling, which is aimed at finding, extracting, and fusing the “semantic”-based user profile from the Web. Previously, Web user profiling was often undertaken by creating a list of...

Discovering Knowledge-Sharing Communities in Question-Answering Forums
Mohamed Bouguessa, Shengrui Wang, Benoit Dumoulin
Article No.: 3
DOI: 10.1145/1870096.1870099

In this article, we define a knowledge-sharing community in a question-answering forum as a set of askers and authoritative users such that, within each community, askers exhibit more homogeneous behavior in terms of their interactions with...

Modeling Social Annotation: A Bayesian Approach
Anon Plangprasopchok, Kristina Lerman
Article No.: 4
DOI: 10.1145/1870096.1870100

Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, for example, Web pages or scientific papers, with descriptive labels called tags. The social annotations contributed by...

Fast Discovery of Group Lag Correlations in Streams
Yasushi Sakurai, Christos Faloutsos, Spiros Papadimitriou
Article No.: 5
DOI: 10.1145/1870096.1870101

The study of data streams has received considerable attention in various communities (theory, databases, data mining, networking), due to several important applications, such as network analysis, sensor monitoring, financial data analysis, and...

A Framework for Computing the Privacy Scores of Users in Online Social Networks
Kun Liu, Evimaria Terzi
Article No.: 6
DOI: 10.1145/1870096.1870102

A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive...