Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two t...
Bin Gao, Tie-Yan Liu, Xin Zheng, QianSheng Cheng, ...
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
Development of advanced anomaly detection and failure diagnosis technologies for spacecraft is a quite significant issue in the space industry, because the space environment is ha...
Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve...
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
The Web has been rapidly "deepened" by myriad searchable databases online, where data are hidden behind query interfaces. As an essential task toward integrating these m...
The popularity of email has triggered researchers to look for ways to help users better organize the enormous amount of information stored in their email folders. One challenge th...
Discovery of sequential patterns is an essential data mining task with broad applications. Among several variations of sequential patterns, closed sequential pattern is the most u...