Distribution Discovery: Local Analysis of Temporal Rules

In recent years, there has been increased interest in using data mining techniques to extract temporal rules from temporal sequences. Local temporal rules, which only a subsequence exhibits, are actually very common in practice. Efficient discovery of the time duration in which temporal rules are valid could benefit KDD of many real applications. In this paper, we present a novel problem class that is the discovery of the distribution of temporal rules. We simplify the mining problem and depict a model that could represent this knowledge clearly, uniquely and efficiently. Our methods include four online dividing strategies for different mining interest, an incremental algorithm for measuring rule-sets, and an algorithm for mining this knowledge. We have analyzed the behavior of the problem and our algorithms with both synthetic data and real data. The results correspond with the definition of our problem and reveal a kind of novel knowledge.