Mining Associations Using Directed Hypergraphs

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 a novel directed hypergraph based model for datab...

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Bibliographic Details
Main Authors: Simha, R., Tripathi, R., Thakur, M.
Format: Conference Proceeding
Language:eng ; jpn
Subjects:
Algorithm design and analysis
Association rules
Clustering algorithms
Data models
Predictive models
Online Access:Request full text
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Summary: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 a novel directed hypergraph based model for databases that 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 an algorithm 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 through experiments on a financial timeseries data set (S&P 500).
DOI:10.1109/ICDEW.2012.56