The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. Maximal frequent mining has triggered much interest since the size of the s...
Lini T. Thomas, Satyanarayana R. Valluri, Kamalaka...
Current outlier detection schemes typically output a numeric score representing the degree to which a given observation is an outlier. We argue that converting the scores into wel...
Manual debugging is expensive. And the high cost has motivated extensive research on automated fault localization in both software engineering and data mining communities. Fault l...
The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much ...
Michele Gorgoglione, Cosimo Palmisano, Alexander T...
We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. B...
There has been a lot of recent interest in mining patterns from graphs. Often, the exact structure of the patterns of interest is not known. This happens, for example, when molecu...
In frequent geographic pattern mining a large amount of patterns is well known a priori. This paper presents a novel approach for mining frequent geographic patterns without assoc...
Recently, there has been considerable interest in computing strongly correlated pairs in large databases. Most previous studies require the specification of a minimum correlation...
In this paper, we propose a novel and general approach for time-series data mining. As an alternative to traditional ways of designing specific algorithm to mine certain kind of ...
Yi Wang, Lizhu Zhou, Jianhua Feng, Jianyong Wang, ...
The immense explosion of geographically referenced data calls for efficient discovery of spatial knowledge. One critical requirement for spatial data mining is the capability to ...
Wei Ding 0003, Christoph F. Eick, Jing Wang 0007, ...