DISCERN: diversity-based selection of centroids for k-estimation and rapid non-stochastic clustering

One of the applications of center-based clustering algorithms such as K-means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a suitable feature space. Nevertheless, while K-means i...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 12; no. 3; pp. 635 - 649
Main Authors: Hassani, Ali, Iranmanesh, Amir, Eftekhari, Mahdi, Salemi, Abbas
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Centroids
Clustering
Complex Systems
Computational Intelligence
Control
Data points
Deep learning
Engineering
Machine learning
Mechatronics
Original Article
Pattern Recognition
Robotics
Systems Biology
Deterministic K-means
Clustering
Estimating the number of clusters
Unsupervised learning
K-means initialization
ISSN:1868-8071, 1868-808X
Online Access:Get full text
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Summary:One of the applications of center-based clustering algorithms such as K-means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a suitable feature space. Nevertheless, while K-means is one of the most efficient offline clustering algorithms, it is not equipped to estimate the number of clusters, which is useful in some practical cases. Other practical methods which do are simply too complex, as they require at least one run of K-means for each possible K . In order to address this issue, we propose a K-means initialization similar to K-means++, which would be able to estimate K based on the feature space while finding suitable initial centroids for K-means in a deterministic manner. Then we compare the proposed method, DISCERN, with a few of the most practical K estimation methods, while also comparing clustering results of K-means when initialized randomly, using K-means++ and using DISCERN. The results show improvement in both the estimation and final clustering performance.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-020-01193-5