Parameter tuning for disjoint clusters based on concept lattices with application to location learning

Brandon M. Hauff, Jitender S. Deogun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish (US)
Title of host publicationRough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings
Pages232-239
Number of pages8
StatePublished - Dec 1 2007
Event11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, RSFDGrC 2007 - Toronto, Canada
Duration: May 14 2007May 17 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4482 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, RSFDGrC 2007
CountryCanada
CityToronto
Period5/14/075/17/07

Fingerprint

Concept Lattice
Parameter Tuning
Cluster Analysis
Disjoint
Tuning
Learning
Clustering
Formal concept analysis
Radio Frequency Identification Device
Radio frequency identification (RFID)
Formal Concept Analysis
Radio Frequency Identification
Personnel
Grouping
Electronics
Databases
Heuristics
Estimate
Range of data

Keywords

  • Clustering
  • Concept lattice
  • Data mining
  • Formal concept analysis
  • Frequent itemsets
  • Location learning
  • Parameter tuning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hauff, B. M., & Deogun, J. S. (2007). Parameter tuning for disjoint clusters based on concept lattices with application to location learning. In Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings (pp. 232-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4482 LNAI).

Parameter tuning for disjoint clusters based on concept lattices with application to location learning. / Hauff, Brandon M.; Deogun, Jitender S.

Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings. 2007. p. 232-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4482 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hauff, BM & Deogun, JS 2007, Parameter tuning for disjoint clusters based on concept lattices with application to location learning. in Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4482 LNAI, pp. 232-239, 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computer, RSFDGrC 2007, Toronto, Canada, 5/14/07.
Hauff BM, Deogun JS. Parameter tuning for disjoint clusters based on concept lattices with application to location learning. In Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings. 2007. p. 232-239. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Hauff, Brandon M. ; Deogun, Jitender S. / Parameter tuning for disjoint clusters based on concept lattices with application to location learning. Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 11th International Conference, RSFDGrC 2007, Proceedings. 2007. pp. 232-239 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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