Parameter Tuning for Disjoint Clusters Based on Concept Lattices with Application to Location Learning

Authors:
Brandon M. Hauff;Jitender S. Deogun
Affiliations:
University of Nebraska - Lincoln, Lincoln, NE 68588-0115,;University of Nebraska - Lincoln, Lincoln, NE 68588-0115,
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

Citing 5
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Review: Formal concept analysis in knowledge processing: A survey on applications

Expert Systems with Applications: An International Journal

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Abstract

Clustering is a technique for grouping items in a dataset that are similar, while separating those items that are dissimilar. The use of concept lattices, from Formal Concept Analysis, for disjoint clustering is a recently studied problem. We develop an algorithm for disjoint clustering of transactional databases using concept lattices. Several heuristics are developed for tuning the support parameters used in this algorithm. Additionally, we discuss the application of this algorithm to Location Learning. In location learning, an object (for example an employee) to be tracked and localized carries an electronic tag, such as an RFID, capable of communicating with some access points that are in the range of the tag. Clustering can then be used to estimate the location of the tag given the signal strengths that can be heard.