Subspace Memory Clustering

• Autorzy: Struski Ł , Tabor J. , Spurek P.

Identyfikatory

DOI

10.4467/20838476SI.15.013.3035

• Języki publikacji: EN

Abstrakty

EN

We present a new subspace clustering method called SuMC (Subspace Memory Clustering), which allows to efficiently divide a dataset D RN into k  N pairwise disjoint clusters of possibly different dimensions. Since our approach is based on the memory compression, we do not need to explicitly specify dimensions of groups: in fact we only need to specify the mean number of scalars which is used to describe a data-point. In the case of one cluster our method reduces to a classical Karhunen-Loeve (PCA) transform. We test our method on some typical data from UCI repository and on data coming from real-life experiments.

Słowa kluczowe

EN

subspace clustering; projection clustering; PCA

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2015
• Tom: Vol. 24
• Strony: 133--142
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Struski Ł

• Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland

autor

Tabor J.

• Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland

autor

Spurek P.

• Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland

Bibliografia

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