51. Improved local search algorithms for Bregman k-means and its variants
- Author
-
Dan Wu, Longkun Guo, Xiaoyun Tian, and Dachuan Xu
- Subjects
021103 operations research ,Control and Optimization ,business.industry ,Applied Mathematics ,0211 other engineering and technologies ,Center (category theory) ,k-means clustering ,Piecewise constant approximation ,0102 computer and information sciences ,02 engineering and technology ,Bregman divergence ,01 natural sciences ,Computer Science Applications ,Computational Theory and Mathematics ,Computer Science::Computational Engineering, Finance, and Science ,010201 computation theory & mathematics ,Discrete Mathematics and Combinatorics ,Local search (optimization) ,business ,Algorithm ,Mathematics - Abstract
In this paper, we consider the Bregman k-means problem (BKM) which is a variant of the classical k-means problem. For an n-point set $${\mathcal {S}}$$ and $$k \le n$$ with respect to $$\mu $$ -similar Bregman divergence, the BKM problem aims first to find a center subset $$C \subseteq {\mathcal {S}}$$ with $$ \mid C \mid = k$$ and then separate $${\mathcal {S}}$$ into k clusters according to C, such that the sum of $$\mu $$ -similar Bregman divergence from each point in $${\mathcal {S}}$$ to its nearest center is minimized. We propose a $$\mu $$ -similar BregMeans++ algorithm by employing the local search scheme, and prove that the algorithm deserves a constant approximation guarantee. Moreover, we extend our algorithm to solve a variant of BKM called noisy $$\mu $$ -similar Bregman k-means++ (noisy $$\mu $$ -BKM++) which is BKM in the noisy scenario. For the same instance and purpose as BKM, we consider the case of sampling a point with an imprecise probability by a factor between $$1-\varepsilon _1$$ and $$1+ \varepsilon _2$$ for $$\varepsilon _1 \in [0,1)$$ and $$\varepsilon _2 \ge 0$$ , and obtain an approximation ratio of $$O(\log ^2 k)$$ in expectation.
- Published
- 2021