Temporal Mining Algorithms: Generalization and Performance Improvements

Temporal Mining Algorithms: Generalization and Performance Improvements Data mining consists of finding interesting trends or patterns in large datasets, in order to guide decisions about future activities. There is a general expectation that data mining tools should be able to identify these patterns in the data with minimal user input. The patterns identified by such tools can give a data analyst useful and unexpected insights that can be more carefully investigated subsequently. The most commonly sought patterns are association rules, that identify a frequently occurring pattern of information in the database. In the first part of research we study the problem of mining clustered association rules. The clustered and the quantitative Association Rules are useful in the context of mining rules over quantitative attributes. Since data used in data mining algorithms is usually temporal, it is very important to discover correlations of attributes over several snapshots. Information like this may affect decisions made in different areas of the business world. We study such problems as: the problem of discovering trend dependencies in temporal data, and temporal sequences mining. The discovered dependencies can be useful for many applications, including: creating special packages of promotions and sales based on customers behavior prediction, creating compact statistical information, and more. In the second part of research we propose some new approaches for mining temporal rules, based on trend dependencies discovery. Several extensions of trend dependency mining algorithms are presented in this thesis, in particular the multi-relational trend dependency mining. Algorithms with proofs of correctness and completeness are given. We also change the definition of support for a trend dependency. The algorithm can be used for mining trend dependencies of different types with variable number of relations, thus it is more general than previous approaches.