Rahal Imad: Vertical Scalabilty For Association Rule Miningr

This work focuses on the data-mining task of association rule mining which discovers association relationships among items in datasets matching user- defined measures of interest. We describe an efficient vertical framework for representing data and mining frequent itemsets that is based on the P- tree technology along with other artificial intelligence techniques, such as set-enumeration trees and tabu search. With the objective of handling the mounting needs of many applications, such as precision agriculture, the proposed framework is used to produce rules in situations where the ubiquitous support-based pruning is not sought. In the context of citation graphs, our proposed framework operates in a (semi) divide-and- conquer parallelized fashion, to discover patterns among subject matters that reveal the evolution history and any possible future extensions of subject matters. The same framework is utilized in an interactive incremental parallel model which focuses on analyzing genome annotation data for association rules potentially useful in annotating new genes, replacing missing values, and validating old annotations.