The DOI matches its Crossref record; 39 of the 41 references it cites were found in a registry or an index.
A Fast Adaptive k-means with No Bounds
Abstract
This paper presents a novel accelerated exact k-means called as "Ball k-means" by using the ball to describe each cluster, which focus on reducing the point-centroid distance computation. The "Ball k-means" can exactly find its neighbor clusters for each cluster, resulting distance computations only between a point and its neighbor clusters' centroids instead of all centroids. What's more, each cluster can be divided into "stable area" and "active area", and the latter one is further divided into some exact "annular area". The assignment of the points in the "stable area" is not changed while the points in each "annular area" will be adjusted within a few neighbor clusters. There are no upper or lower bounds in the whole process. Moreover, ball k-means uses ball clusters and neighbor searching along with multiple novel stratagems for reducing centroid distance computations. In comparison with the current state-of-the art accelerated exact bounded methods, the Yinyang algorithm and the Exponion algorithm, as well as other top-of-the-line tree-based and bounded methods, the ball k-means attains both higher performance and performs fewer distance calculations, especially for large-k problems. The faster speed, no extra parameters and simpler design of "Ball k-means" make it an all-around replacement of the naive k-means.
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UCI Machine Learning Repository
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Research works printed but not linked: 1
- Ortega, 2018, A-means: Improving the cluster assignment phase of k-means for big data, Int. J. Combinatorial Optim. Problems Inform., 9, 3
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Xia, S., Dai, X., Wang, G., Gao, X., & Giem, E. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3203381 · Cited by 158
Self-citationXu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164
A Fast Granular-Ball-Based Density Peaks Clustering Algorithm for Large-Scale Data
Cheng, D., Li, Y., Xia, S., Wang, G., Huang, J., & Zhang, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3300916 · Cited by 141
Self-citationFast LDP-MST: An Efficient Density-Peak-Based Clustering Method for Large-Size Datasets
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K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters
Cheng, D., Huang, J., Zhang, S., Xia, S., Wang, G., & Xie, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3248064 · Cited by 85
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Metadata
- Authors
- Xia, S., Peng, D., Meng, D., Zhang, C., Wang, G., Giem, E., Wei, W., & Chen, Z.
- Affiliations
- Chongqing University of Posts and Telecommunications (CN)
- Macau University of Science and Technology (MO)
- Tianjin University (CN)
- University of California, Riverside (US)
- Xi'an University of Technology (CN)
- ORCID
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2020
- Volume
- 44
- Issue
- 1
- Pages
- 1-1
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3043317456
- Cited by
- 149
- References
- 41
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 34 · 2023: 19 · 2022: 34 · 2021: 24 · 2020: 7
- FWCI
- 8.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0485 and cstc2019jcyj-cxttX0002)
- National Basic Research Program of China (2019QY(Y)0301 and 2016QY01W0200)
- National Natural Science Foundation of China (61806030& 61876027&61772096&61533020)
- NICE: NRT for Integrated Computational Entomology US NSF award (1631776)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- K-means clustering, annular area
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