@inproceedings{af9224a111cb45f4b2bf161ce6e8cdf3,
title = "Parameter tuning for disjoint clusters based on concept lattices with application to location learning",
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.",
keywords = "Clustering, Concept lattice, Data mining, Formal concept analysis, Frequent itemsets, Location learning, Parameter tuning",
author = "Hauff, {Brandon M.} and Deogun, {Jitender S.}",
year = "2007",
doi = "10.1007/978-3-540-72530-5_27",
language = "English (US)",
isbn = "9783540725299",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "232--239",
booktitle = "Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings",
note = "11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, RSFDGrC 2007 ; Conference date: 14-05-2007 Through 17-05-2007",
}