We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...
Several pattern discovery methods proposed in the data mining literature have the drawbacks that they discover too many obvious or irrelevant patterns and that they do not leverag...
This paper shows preliminary results, on financial data, of an algorithm for discovering pairs of an exception rule and a common sense rule under a prespecified schedule. An exce...
Active responses from experts play an essential role in the knowledge discovery of SAR (structure activity relationships) from drug data. Experts often think of hypotheses, and the...
We present D-HOTM, a framework for Distributed Higher Order Text Mining based on named entities extracted from textual data that are stored in distributed relational databases. Unl...