Mining Associations Using Directed Hypergraphs

• Main Author: Simha, Ramanuja N.
• Format: Others
• Published: Scholar Commons 2011
• Subjects: Association Rules; Clustering; Discretization; Financial Time-series; Multi-valued Attributes; Similarity; American Studies; Arts and Humanities; Computer Sciences
• Online Access: http://scholarcommons.usf.edu/etd/3345; http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=4540&context=etd

This thesis proposes a novel directed hypergraph based model for any database. We introduce the notion of association rules for multi-valued attributes, which is an adaptation of the definition of quantitative association rules known in the literature. The association rules for multi-valued attributes are integrated in building the directed hypergraph model. This model allows to capture attribute-level associations and their strength. Basing on this model, we provide association-based similarity notions between any two attributes and present a method for finding clusters of similar attributes. We then propose algorithms to identify a subset of attributes known as a leading indicator that influences the values of almost all other attributes. Finally, we present an association-based classifier that can be used to predict values of attributes. We demonstrate the effectiveness of our proposed model, notions, algorithms, and classifier through experiments on a financial time-series data set (S&P 500).