An important problem in data mining is detecting changes in large data sets. Although there are a variety of change detection algorithms that have been developed, in practice it c...
Chris Curry, Robert L. Grossman, David Locke, Stev...
It is becoming increasingly common to construct databases from information automatically culled from many heterogeneous sources. For example, a research publication database can b...
Aron Culotta, Michael L. Wick, Robert Hall, Matthe...
Users attempt to express their search goals through web search queries. When a search goal has multiple components or aspects, documents that represent all the aspects are likely ...
This article tries to give an answer to a fundamental question in temporal data mining: "Under what conditions a temporal rule extracted from up-to-date temporal data keeps i...
The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel tech...
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Existing data-stream clustering algorithms such as CluStream are based on k-means. These clustering algorithms are incompetent to find clusters of arbitrary shapes and cannot hand...