Using MM principles to deal with incomplete data in K-means clustering
- URL: http://arxiv.org/abs/2212.12379v1
- Date: Fri, 23 Dec 2022 14:50:57 GMT
- Title: Using MM principles to deal with incomplete data in K-means clustering
- Authors: Ali Beikmohammadi
- Abstract summary: K-means clustering algorithm is widely used because of its simple algorithm and fast convergence.
We mainly apply MM principles to restore the symmetry of the data, so that K-means could work well.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Among many clustering algorithms, the K-means clustering algorithm is widely
used because of its simple algorithm and fast convergence. However, this
algorithm suffers from incomplete data, where some samples have missed some of
their attributes. To solve this problem, we mainly apply MM principles to
restore the symmetry of the data, so that K-means could work well. We give the
pseudo-code of the algorithm and use the standard datasets for experimental
verification. The source code for the experiments is publicly available in the
following link:
\url{https://github.com/AliBeikmohammadi/MM-Optimization/blob/main/mini-project/MM%20K-means.ipynb}.
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