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Knowledge Discovery from Data (TKDD)

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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 7 Issue 2, July 2013

Learning to predict reciprocity and triadic closure in social networks
Tiancheng Lou, Jie Tang, John Hopcroft, Zhanpeng Fang, Xiaowen Ding
Article No.: 5
DOI: 10.1145/2499907.2499908

We study how links are formed in social networks. In particular, we focus on investigating how a reciprocal (two-way) link, the basic relationship in social networks, is developed from a parasocial (one-way) relationship and how the relationships...

Efficient online learning for multitask feature selection
Haiqin Yang, Michael R. Lyu, Irwin King
Article No.: 6
DOI: 10.1145/2499907.2499909

Learning explanatory features across multiple related tasks, or MultiTask Feature Selection (MTFS), is an important problem in the applications of data mining, machine learning, and bioinformatics. Previous MTFS methods fulfill this task by...

Multilabel relationship learning
Yu Zhang, Dit-Yan Yeung
Article No.: 7
DOI: 10.1145/2499907.2499910

Multilabel learning problems are commonly found in many applications. A characteristic shared by many multilabel learning problems is that some labels have significant correlations between them. In this article, we propose a novel multilabel...

Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction
Jing Peng, Guna Seetharaman, Wei Fan, Aparna Varde
Article No.: 8
DOI: 10.1145/2499907.2499911

Knowledge discovery from big data demands effective representation of data. However, big data are often characterized by high dimensionality, which makes knowledge discovery more difficult. Many techniques for dimensionality reudction have been...