Combining multiple clusterings arises in various important data mining scenarios. However, finding a consensus clustering from multiple clusterings is a challenging task because ...
In this paper, we propose a text representation model, Tensor Space Model (TSM), which models the text by multilinear algebraic high-order tensor instead of the traditional vector...
Ning Liu, Benyu Zhang, Jun Yan, Zheng Chen, Wenyin...
In this paper we study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals. We assume that the database consists of sequences of even...
Panagiotis Papapetrou, George Kollios, Stan Sclaro...
Consider spatial data consisting of a set of binary features taking values over a collection of spatial extents (grid cells). We propose a method that simultaneously finds spatia...
In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. The massive data volume poses challenges to both human users and comp...
Feng Pan, Wei Wang 0010, Anthony K. H. Tung, Jiong...
Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert)...
Data mining focuses on patterns that summarize the data. In this paper, we focus on mining patterns that could change the state by responding to opportunities of actions.
Yuelong Jiang, Ke Wang, Alexander Tuzhilin, Ada Wa...
In data mining, enumerate the frequent or the closed patterns is often the first difficult task leading to the association rules discovery. The number of these patterns represen...
Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting an array in up to K segments. We want segments to be as monotonic as po...
We address the issue of providing highly informative and comprehensive annotations using information revealed by the structured vocabularies of Gene Ontology (GO). For a target, a...