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An Integrated Approach to Mining Data Streams

The biggest shortcoming of efficient Bayesian learning is the attribute independence assumption. Current state-ofthe-art learners weaken this to capture pairwise correlations with quadratic efficiency. This paper details a recent advance in mining correlated data through the integration of techniques from the fields of outlier detection, clustering and supervised learning. The algorithm described here is able to learn and exploit correlations between a potentially unbounded number of attributes in one pass over the data, with strictly bounded memory and processing costs.