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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 consider joint genetic effects across multiple...
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 article has two goals; The first goal is exact and...
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 entire event sequence. Moreover, the number of...
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. However, sometimes the advantage of exploiting the...
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 graphs from a static point of view. The study of...
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 well, while simultaneously not deviate too...
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