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2015 | 24 |
Article title

Subspace Memory Clustering

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PL
Abstracts
PL
We present a new subspace clustering method called SuMC (Subspace Memory Clustering), which allows to efficiently divide a dataset D RN into k 2 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.
Publisher
Year
Volume
24
Physical description
Dates
published
2015
online
06 - 07 - 2016
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Publication order reference
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YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2015-volume-24-article-6341
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