Description

Predicting the next element(s) of a sequence is a research problem with wide applications such as stock market prediction, consumer product recommendation, and web link recommendation. Techniques for sequence prediction can be categorized according to the types of sequences on which they are applied. These rules are used to make predictions for new sequences. These rules are used to predict the webpages that news users will visit. There are two main types. On one hand, time series are sequences of numeric data typically recorded at an equal time interval. On the other hand, symbolic sequences are sequences of events or nominal data generally recorded at unequal time intervals. This system presents an idea of mining “partially-ordered sequential rules” (POSR) in this project. This project proposes the RuleGrowth algorithm to mine POSR. A more general form of sequential rules common to multiple sequences such that items in the antecedent and in the consequent of each rule are unordered. It uses a novel approach named “rule expansions” to generate sequential rules and includes several strategies to perform the search efficiently. RuleGrowth is easily extendable. Constraints can be added to the algorithm for the needs of specific applications. Experiments also show that the proposed algorithms have excellent scalability.