Approximating K‐means‐type Clustering via Semidefinite Programming
نویسندگان
چکیده
منابع مشابه
Approximating K-means-type Clustering via Semidefinite Programming
One of the fundamental clustering problems is to assign n points into k clusters based on the minimal sum-of-squares(MSSC), which is known to be NP-hard. In this paper, by using matrix arguments, we first model MSSC as a so-called 0-1 semidefinite programming (SDP). We show that our 0-1 SDP model provides an unified framework for several clustering approaches such as normalized k-cut and spectr...
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One of the fundamental clustering problems is to assign n points into k clusters based on the minimal sum-of-squares(MSSC), which is known to be NP-hard. In this paper, by using matrix arguments, we first model MSSC as a so-called 0-1 semidefinite programming (SDP). We show that our 0-1 SDP model provides an unified framework for several clustering approaches such as normalized k-cut and spectr...
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ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2007
ISSN: 1052-6234,1095-7189
DOI: 10.1137/050641983