finding frequent patterns plays a key role in exploring association patterns, correlation, and many other interesting relationships that are applicable in tdb. several association rule mining algorithms such as apriori, fp-growth, and eclat have been proposed in the literature. fp-growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract...

Frequent pattern mining has been a focused theme in data mining research and an important first step in the analysis of data arising in a broad range of applications. The traditional exact model for frequent pattern requires that every item occurs in each supporting transaction. However, real application data is usually subject to random noise or measurement error, which poses new challenges fo...

Cubes and association rules discover frequent patterns in a data set, most of which are not significant. Thus previous research has introduced search constraints and statistical metrics to discover significant patterns and reduce processing time. We introduce cube pairs (comparing cube groups based on a parametric statistical test) and rule pairs (based on two similar association rules), which ...

Proefschrift voorgelegd tot het behalen van de graad van Doctor in de Wetenschappen, richting Informatica te verdedigen door Acknowledgements Many people have contributed to the realization of this thesis. First an foremost, I am grateful to my advisor Jan Van den Bussche for his guidance throughout my doctoral studies and all the time and effort he put in the development of me and my work. The...

A method for mining frequent patterns of individual user trajectories is proposed based on location semantics. The semantic trajectory obtained by inverse geocoding and preprocessed to obtain the Top-k candidate item sets, then spatio-temporal sequence intersection divide conquer merge methods are used convert iterative calculation long itemsets into hierarchical sets' regular operations, super...