Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2021
Abstract
In this paper, we revisit the decades-old clustering method k-means. The egg-chicken loop in traditional k-means has been replaced by a pure stochastic optimization procedure. The optimization is undertaken from the perspective of each individual sample. Different from existing incremental k-means, an individual sample is tentatively joined into a new cluster to evaluate its distance to the corresponding new centroid, in which the contribution from this sample is accounted. The sample is moved to this new cluster concretely only after we find the reallocation makes the sample closer to the new centroid than it is to the current one. Compared with traditional k-means and other variants, this new procedure allows the clustering to converge faster to a better local minimum. This fundamental modification over the k-means loop leads to the redefinition of a family of k-means variants, such as hierarchical k-means, and Sequential k-means. As an extension, a new target function that minimizes the summation of pairwise distances within clusters is presented. Under l2-norm, it could be solved under the same stochastic optimization procedure. The re-defined traditional k-means, hierarchical k-means, as well as Sequential kmeans all show considerable performance improvement over their traditional counterparts under different settings and on various types of datasets
Keywords
Driven function, k-means, Stochastic optimization
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Conference, November 1-5
First Page
2679
Last Page
2687
ISBN
9781450384469
Identifier
10.1145/3459637.3482359
Publisher
ACM
City or Country
New York
Citation
ZHAO Wan-Lei; LAN, Shi Ying; CHEN, Run-Qing; and NGO, Chong-wah.
K-sums clustering: A stochastic optimization approach. (2021). CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Conference, November 1-5. 2679-2687.
Available at: https://ink.library.smu.edu.sg/sis_research/6806
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3459637.3482359