Affiliations: Department of Computer Science and Engineering,
University of Nebraska – Lincoln, Lincoln, NE, 68588-0115, USA.
E-mail: {ljiang,deogun}@cse.unl.edu
Abstract: Formal concept analysis and probability logic are two useful tools
for data analysis. Data is usually represented as a two-dimensional context of
objects and features. FCA discovers dependencies within the data based on the
relation among objects and features. On the other hand, the probability logic
represents and reasons with both statistical and propositional probability
among data. We propose SPICE – Symbolic integration of Probability
Inference and Concept Extraction, which provides a more flexible and robust
framework for data mining tasks. Within SPICE, we formalize the important
notions of data mining, such as concepts and patterns, and develop new notions
such as maximal potentially useful patterns. In this paper, we formalize the
association rule mining in SPICE and propose an enhanced rule mining approach,
called SPICE association rule mining, to solve the problem of time inefficiency
and rule redundancy in general association rule mining. We show an application
of the SPICE approach in the Geo-spatial Decision Support System (GDSS). The
experimental results show that SPICE can efficiently and effectively discover a
succinct set of interesting association rules.
Keywords: FCA, probability logic, data mining, association rules, redundant rules, important items
