We pose a fundamental question in understanding how to identify and design successful communities: What factors predict whether a community will grow and survive in the long term?...
Abstract. One of the de ning challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, ...
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...
In this paper, we present a stream-based mining algorithm for online anomaly prediction. Many real-world applications such as data stream analysis requires continuous cluster opera...
We present an ensemble learning approach that achieves accurate predictions from arbitrarily partitioned data. The partitions come from the distributed processing requirements of ...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...