Abstract
K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize. Thus, this paper proposes a balanced k-Means method, which means number of items distributed within clusters are more balanced, provide more equal-sized clusters. Cases those are suitable to apply this method are also discussed, such as Travelling Salesman Problem (TSP). In order to enhance the performance and usability, we are in the process of proposing a learning ability of this method in the future.
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Notes
- 1.
Travelling salesman problem, https://en.wikipedia.org/wiki/Travelling_salesman_problem.
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Tai, CL., Wang, CS. (2017). Balanced k-Means. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_8
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DOI: https://doi.org/10.1007/978-3-319-54430-4_8
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