Unsupervised clustering of symbol strings and context recognition

John A. Flanagan, Jani Mäntyjarvi, Johan Himberg

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

21 Citations (Scopus)

Abstract

The representation of information based on symbol strings has been applied to the recognition of context. A framework for approaching the context recognition problem has been described and interpreted in terms of symbol string recognition. The Symbol String Clustering Map (SCM) is introduced as an efficient algorithm for the unsupervised clustering and recognition of symbol string data. The SCM can be implemented in an on line manner using a computationally simple similarity measure based on a weighted average. It is shown how measured sensor data can be processed by the SCM algorithm to learn, represent and distinguish different user contexts without any user input.

Original languageEnglish
Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages171-178
Number of pages8
ISBN (Print)978-076951754-4
Publication statusPublished - 1 Dec 2002
MoE publication typeNot Eligible
Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
Duration: 9 Dec 200212 Dec 2002

Conference

Conference2nd IEEE International Conference on Data Mining, ICDM '02
CountryJapan
CityMaebashi
Period9/12/0212/12/02

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  • Cite this

    Flanagan, J. A., Mäntyjarvi, J., & Himberg, J. (2002). Unsupervised clustering of symbol strings and context recognition. In Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002 (pp. 171-178). IEEE Institute of Electrical and Electronic Engineers.