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.
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© 2007 Springer-Verlag Berlin Heidelberg
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Hauff, B.M., Deogun, J.S. (2007). Parameter Tuning for Disjoint Clusters Based on Concept Lattices with Application to Location Learning. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_27
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DOI: https://doi.org/10.1007/978-3-540-72530-5_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72529-9
Online ISBN: 978-3-540-72530-5
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