The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
How much can k-means be improved by using better initialization and repeats?
Open accessAbstract
In this paper, we study what are the most important factors that deteriorate the performance of the k-means algorithm, and how much this deterioration can be overcome either by using a better initialization technique, or by repeating (restarting) the algorithm. Our main finding is that when the clusters overlap, k-means can be significantly improved using these two tricks. Simple furthest point heuristic (Maxmin) reduces the number of erroneous clusters from 15% to 6%, on average, with our clustering benchmark. Repeating the algorithm 100 times reduces it further down to 1%. This accuracy is more than enough for most pattern recognition applications. However, when the data has well separated clusters, the performance of k-means depends completely on the goodness of the initialization. Therefore, if high clustering accuracy is needed, a better algorithm should be used instead.
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Cites
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Cited by
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023) · Information Sciences · doi:10.1016/j.ins.2022.11.139 · Cited by 2,272
Metadata
- Authors
- Fränti, P., & Sieranoja, S.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 93
- Pages
- 95-112
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2940320374
- Cited by
- 451
- References
- 70
- FWCI
- 24.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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