کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532426 869952 2011 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
State-space dynamics distance for clustering sequential data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
State-space dynamics distance for clustering sequential data
چکیده انگلیسی

This paper proposes a novel similarity measure for clustering sequential data. We first construct a common state space by training a single probabilistic model with all the sequences in order to get a unified representation for the dataset. Then, distances are obtained attending to the transition matrices induced by each sequence in that state space. This approach solves some of the usual overfitting and scalability issues of the existing semi-parametric techniques that rely on training a model for each sequence. Empirical studies on both synthetic and real-world datasets illustrate the advantages of the proposed similarity measure for clustering sequences.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 44, Issue 5, May 2011, Pages 1014–1022
نویسندگان
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