Knowledge Discovery from Data (TKDD)


Search Issue
enter search term and/or author name


ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 9 Issue 2, November 2014

Efficiently Estimating Motif Statistics of Large Networks
Pinghui Wang, John C. S. Lui, Bruno Ribeiro, Don Towsley, Junzhou Zhao, Xiaohong Guan
Article No.: 8
DOI: 10.1145/2629564

Exploring statistics of locally connected subgraph patterns (also known as network motifs) has helped researchers better understand the structure and function of biological and Online Social Networks (OSNs). Nowadays, the massive size of some...

A Framework for Hierarchical Ensemble Clustering
Li Zheng, Tao Li, Chris Ding
Article No.: 9
DOI: 10.1145/2611380

Ensemble clustering, as an important extension of the clustering problem, refers to the problem of combining different (input) clusterings of a given dataset to generate a final (consensus) clustering that is a better fit in some sense than...

Toward Personalized Context Recognition for Mobile Users: A Semisupervised Bayesian HMM Approach
Baoxing Huai, Enhong Chen, Hengshu Zhu, Hui Xiong, Tengfei Bao, Qi Liu, Jilei Tian
Article No.: 10
DOI: 10.1145/2629504

The problem of mobile context recognition targets the identification of semantic meaning of context in a mobile environment. This plays an important role in understanding mobile user behaviors and thus provides the opportunity for the development...

Anomaly Detection from Incomplete Data
Siyuan Liu, Lei Chen, Lionel M. Ni
Article No.: 11
DOI: 10.1145/2629668

Anomaly detection (a.k.a., outlier or burst detection) is a well-motivated problem and a major data mining and knowledge discovery task. In this article, we study the problem of population anomaly detection, one of the key issues related to event...

User Vulnerability and Its Reduction on a Social Networking Site
Pritam Gundecha, Geoffrey Barbier, Jiliang Tang, Huan Liu
Article No.: 12
DOI: 10.1145/2630421

Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breaches with dire consequences. With the...

Selecting the Right Correlation Measure for Binary Data
Lian Duan, W. Nick Street, Yanchi Liu, Songhua Xu, Brook Wu
Article No.: 13
DOI: 10.1145/2637484

Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. Although there are numerous measures available for evaluating correlations, different correlation measures provide...

Physics-Based Anomaly Detection Defined on Manifold Space
Hao Huang, Hong Qin, Shinjae Yoo, Dantong Yu
Article No.: 14
DOI: 10.1145/2641574

Current popular anomaly detection algorithms are capable of detecting global anomalies but often fail to distinguish local anomalies from normal instances. Inspired by contemporary physics theory (i.e., heat diffusion and quantum mechanics), we...