Knowledge Discovery from Data (TKDD)


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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 1 Issue 4, January 2008

Topic taxonomy adaptation for group profiling
Lei Tang, Huan Liu, Jianping Zhang, Nitin Agarwal, John J. Salerno
Article No.: 1
DOI: 10.1145/1324172.1324173

A topic taxonomy is an effective representation that describes salient features of virtual groups or online communities. A topic taxonomy consists of topic nodes. Each internal node is defined by its vertical path (i.e., ancestor and child nodes)...

Finding hierarchical heavy hitters in streaming data
Graham Cormode, Flip Korn, S. Muthukrishnan, Divesh Srivastava
Article No.: 2
DOI: 10.1145/1324172.1324174

Data items that arrive online as streams typically have attributes which take values from one or more hierarchies (time and geographic location, source and destination IP addresses, etc.). Providing an aggregate view of such data is important for...

Learning correlations using the mixture-of-subsets model
Manas Somaiya, Christopher Jermaine, Sanjay Ranka
Article No.: 3
DOI: 10.1145/1324172.1324175

Using a mixture of random variables to model data is a tried-and-tested method common in data mining, machine learning, and statistics. By using mixture modeling it is often possible to accurately model even complex, multimodal data via very...

A clustering framework based on subjective and objective validity criteria
M. Halkidi, D. Gunopulos, M. Vazirgiannis, N. Kumar, C. Domeniconi
Article No.: 4
DOI: 10.1145/1324172.1324176

Clustering, as an unsupervised learning process is a challenging problem, especially in cases of high-dimensional datasets. Clustering result quality can benefit from user constraints and objective validity assessment. In this article, we propose...