Degree Granting Department
Computer Science and Engineering
Rahul Tripathi, Ph.D.
Yicheng Tu, Ph.D.
Xiaoning Qian, Ph.D.
Association Rules, Multi-valued Attributes, Financial Time-series, Discretization, Clustering, Similarity, Dominator
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).
Scholar Commons Citation
Simha, Ramanuja N., "Mining Associations Using Directed Hypergraphs" (2011). Graduate Theses and Dissertations.