Space-Time Clustering with Stability Probe while Riding Downhill
Document Type
Article
Publication Date
1-1-2016
Department
Political Science, International Development, and International Affairs
School
Social Science and Global Studies
Abstract
We propose a new data-driven procedure of optimal selection of tuning parameters in dynamic clustering algorithms, using the notion of stability probe. Due to the shape of the stability probe dynamics, we refer to the new clustering stability procedure as Downhill Riding (DR). We study final sample performance of DR in conjunction with DBSCAN and TRUST in application to clustering synthetic times series and yearly temperature records in Central Germany.
Publication Title
SIGKDD Mining and Learning from Time Series (MiLeTS2016)
Recommended Citation
Huang, X.,
Iliev, I. R.,
Brenning, A.,
Gel, Y. R.
(2016). Space-Time Clustering with Stability Probe while Riding Downhill. SIGKDD Mining and Learning from Time Series (MiLeTS2016).
Available at: https://aquila.usm.edu/fac_pubs/19778
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