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Knowledge Discovery from Data (TKDD)

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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 4 Issue 1, January 2010

Factor in the neighbors: Scalable and accurate collaborative filtering
Yehuda Koren
Article No.: 1
DOI: 10.1145/1644873.1644874

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on collaborating filtering (CF), where past transactions are analyzed in order to establish connections between users and products....

Motif discovery in physiological datasets: A methodology for inferring predictive elements
Zeeshan Syed, Collin Stultz, Manolis Kellis, Piotr Indyk, John Guttag
Article No.: 2
DOI: 10.1145/1644873.1644875

In this article, we propose a methodology for identifying predictive physiological patterns in the absence of prior knowledge. We use the principle of conservation to identify activity that consistently precedes an outcome in patients, and...

Self-sufficient itemsets: An approach to screening potentially interesting associations between items
Geoffrey I. Webb
Article No.: 3
DOI: 10.1145/1644873.1644876

Self-sufficient itemsets are those whose frequency cannot be explained solely by the frequency of either their subsets or of their supersets. We argue that itemsets that are not self-sufficient will often be of little interest to the data analyst,...

Mining multidimensional and multilevel sequential patterns
Marc Plantevit, Anne Laurent, Dominique Laurent, Maguelonne Teisseire, Yeow WEI Choong
Article No.: 4
DOI: 10.1145/1644873.1644877

Multidimensional databases have been designed to provide decision makers with the necessary tools to help them understand their data. This framework is different from transactional data as the datasets contain huge volumes of historicized and...

VOGUE: A variable order hidden Markov model with duration based on frequent sequence mining
Mohammed J. Zaki, Christopher D. Carothers, Boleslaw K. Szymanski
Article No.: 5
DOI: 10.1145/1644873.1644878

We present VOGUE, a novel, variable order hidden Markov model with state durations, that combines two separate techniques for modeling complex patterns in sequential data: pattern mining and data modeling. VOGUE relies on a variable gap sequence...