通过基于仔细播种的进化技术提高 K-Means 和随机交换的聚类精度

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Algorithms Pub Date : 2023-12-17 DOI:10.3390/a16120572
L. Nigro, F. Cicirelli
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引用次数: 0

摘要

K-Means 算法简单高效,是 "事实上 "的标准聚类算法。不过,K-Means 严重依赖于中心点的初始化(播种方法),经常会陷入局部次优解。事实上,K-Means 主要充当中心点的局部细化器,无法将中心点移动到整个数据空间。随机交换的定义超越了 K-Means,其工作方式是将 K-Means 整合到中心点管理的全局策略中,这通常能产生接近全局最优的聚类解决方案。本文提出了一种扩展 K-Means 和随机交换的方法,并通过进化技术和精心播种提高了聚类精度。本文提出了两种新算法:基于种群的 K-Means 算法(PB-KM)和基于种群的随机交换算法(PB-RS)。这两种算法都包括两个步骤:首先,建立一个由 J 个候选解组成的群体,然后对候选中心点进行反复重组,以获得最终的精确解。论文介绍了 PB-KM 和 PB-RS 的设计动机,概述了它们目前基于并行流的 Java 实现,并使用合成数据集和真实数据集演示了可实现的聚类精度。
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Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding
K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-Means, in fact, mainly acts as a local refiner of the centroids, and it is unable to move centroids all over the data space. Random Swap was defined to go beyond K-Means, and its modus operandi integrates K-Means in a global strategy of centroids management, which can often generate a clustering solution close to the global optimum. This paper proposes an approach which extends both K-Means and Random Swap and improves the clustering accuracy through an evolutionary technique and careful seeding. Two new algorithms are proposed: the Population-Based K-Means (PB-KM) and the Population-Based Random Swap (PB-RS). Both algorithms consist of two steps: first, a population of J candidate solutions is built, and then the candidate centroids are repeatedly recombined toward a final accurate solution. The paper motivates the design of PB-KM and PB-RS, outlines their current implementation in Java based on parallel streams, and demonstrates the achievable clustering accuracy using both synthetic and real-world datasets.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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