Presentation 2007-03-05
Data Clustering by Collective Synchronization in an Ensemble of Multivariate Data
Takaya MIYANO, Takako TSUTSUI,
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Abstract(in English) We propose a new method of data mining based on an analogue of the Kuramoto model. In the method, multivariate data are input to the natural frequencies of phase oscillators that form a local network through short-range interactions between phase vectors. Common frequencies of groups of phase oscillators attained by partial phase-locking are interpreted as major templates representing general features of the multivariate data set. This method was applied to the national database of care needs certification for the Japanese public long-term care insurance program. Data syncronization was achieved and generated three major patterns in the ageing process of the frail elderly.
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Keyword(in English) Collective Synchronization / Phase Oscillator / Data Mining / Medical Infomation
Paper # NLP2006-148
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Conference Information
Committee NLP
Conference Date 2007/2/26(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Data Clustering by Collective Synchronization in an Ensemble of Multivariate Data
Sub Title (in English)
Keyword(1) Collective Synchronization
Keyword(2) Phase Oscillator
Keyword(3) Data Mining
Keyword(4) Medical Infomation
1st Author's Name Takaya MIYANO
1st Author's Affiliation Faculty of Science and Engineering, Ritsumeikan University()
2nd Author's Name Takako TSUTSUI
2nd Author's Affiliation National Institute of Public Health
Date 2007-03-05
Paper # NLP2006-148
Volume (vol) vol.106
Number (no) 573
Page pp.pp.-
#Pages 5
Date of Issue