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Projected memory clustering
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AI
DOI：10.1016/j.patrec.2019.02.023.png)
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Abstract
En 中文
We present a new algorithm PMC (Projected Memory Clustering) for projected clustering of high dimensional data. It effectively discovers clusters described by affine subspaces parallel to the main axes of coordinate system. The number of clusters and dimensions of subspaces are selected automatically. Experiments performed on various types of data show that PMC detects clustering structures better than related projected clustering methods. Moreover, it is fast, which makes it suitable for practical use. (C) 2019 Published by Elsevier B.V.
Keywords:
Projected clustering
Subspaces clustering
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