Knowledge Discovery from Data (TKDD)


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ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 3 Issue 4, November 2009

ACM TKDD special issue ACM SIGKDD 2007 and ACM SIGKDD 2008
Heikki Mannila, Dimitrios Gunopulos
Article No.: 15
DOI: 10.1145/1631162.1631163

An event-based framework for characterizing the evolutionary behavior of interaction graphs
Sitaram Asur, Srinivasan Parthasarathy, Duygu Ucar
Article No.: 16
DOI: 10.1145/1631162.1631164

Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these...

On evolutionary spectral clustering
Yun Chi, Xiaodan Song, Dengyong Zhou, Koji Hino, Belle L. Tseng
Article No.: 17
DOI: 10.1145/1631162.1631165

Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data...

Fast likelihood search for hidden Markov models
Yasuhiro Fujiwara, Yasushi Sakurai, Masaru Kitsuregawa
Article No.: 18
DOI: 10.1145/1631162.1631166

Hidden Markov models (HMMs) are receiving considerable attention in various communities and many applications that use HMMs have emerged such as mental task classification, biological analysis, traffic monitoring, and anomaly detection. This...

Efficient algorithms for genome-wide association study
Xiang Zhang, Fei Zou, Wei Wang
Article No.: 19
DOI: 10.1145/1631162.1631167

Studying the association between quantitative phenotype (such as height or weight) and single nucleotide polymorphisms (SNPs) is an important problem in biology. To understand underlying mechanisms of complex phenotypes, it is often necessary to...

Reflect and correct: A misclassification prediction approach to active inference
Mustafa Bilgic, Lise Getoor
Article No.: 20
DOI: 10.1145/1631162.1631168

Information diffusion, viral marketing, graph-based semi-supervised learning, and collective classification all attempt to model and exploit the relationships among nodes in a network to improve the performance of node labeling algorithms....

Constructing comprehensive summaries of large event sequences
Jerry Kiernan, Evimaria Terzi
Article No.: 21
DOI: 10.1145/1631162.1631169

Event sequences capture system and user activity over time. Prior research on sequence mining has mostly focused on discovering local patterns appearing in a sequence. While interesting, these patterns do not give a comprehensive summary of the...