Graduation Year
2011
Document Type
Thesis
Degree
M.S.Cp.E.
Degree Granting Department
Computer Science and Engineering
Major Professor
Rahul Tripathi, Ph.D.
Committee Member
Yicheng Tu, Ph.D.
Committee Member
Xiaoning Qian, Ph.D.
Keywords
Association Rules, Multi-valued Attributes, Financial Time-series, Discretization, Clustering, Similarity, Dominator
Abstract
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). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/3345
