Unsupervised clustering of symbol strings and context recognition

John A. Flanagan; Jani Mäntyjarvi; Johan Himberg

Unsupervised clustering of symbol strings and context recognition

John A. Flanagan, Jani Mäntyjarvi, Johan Himberg

Not published at VTT

Nokia Oyj

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

24 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 language	English
Title of host publication	Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	171-178
Number of pages	8
ISBN (Print)	978-076951754-4
Publication status	Published - 1 Dec 2002
MoE publication type	Not Eligible
Event	2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan Duration: 9 Dec 2002 → 12 Dec 2002

Conference

Conference	2nd IEEE International Conference on Data Mining, ICDM '02
Country/Territory	Japan
City	Maebashi
Period	9/12/02 → 12/12/02

Cite this

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title = "Unsupervised clustering of symbol strings and context recognition",

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.",

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booktitle = "Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002",

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note = "2nd IEEE International Conference on Data Mining, ICDM '02 ; Conference date: 09-12-2002 Through 12-12-2002",

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Unsupervised clustering of symbol strings and context recognition. / Flanagan, John A.; Mäntyjarvi, Jani; Himberg, Johan.
Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002. IEEE Institute of Electrical and Electronic Engineers, 2002. p. 171-178.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Unsupervised clustering of symbol strings and context recognition

AU - Flanagan, John A.

AU - Mäntyjarvi, Jani

AU - Himberg, Johan

PY - 2002/12/1

Y1 - 2002/12/1

N2 - 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.

AB - 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.

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M3 - Conference article in proceedings

AN - SCOPUS:0041392557

SN - 978-076951754-4

SP - 171

EP - 178

BT - Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 2nd IEEE International Conference on Data Mining, ICDM '02

Y2 - 9 December 2002 through 12 December 2002

ER -

Unsupervised clustering of symbol strings and context recognition

Abstract

Conference

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