基于特征加权的K-Means算法综述
A survey on feature weighting based K-Means algorithms
摘要：
在现实世界的数据集中，总是存在这样的可能性，我们认为这种可能性相当高，即不同的特征可能具有不同程度的相关性。大多数机器学习算法通过在数据预处理阶段选择或取消选择特征来处理这一事实。然而，我们认为，即使在相关的功能可能有不同程度的相关性，这应该考虑到在聚类过程中。拥有超过50年的历史，K-Means可以说是最流行的分区聚类算法。第一个基于K-Means计算特征权重的聚类算法是在30多年前设计的。各种这样的算法已经被设计，但还没有，据我们所知，一项调查整合集群恢复能力的经验证据，常见的缺陷，以及未来研究的可能方向。本文详细阐述了特征加权的概念，并通过批判性地分析一些最流行的，或创新的，基于K-Means的特征加权机制来解决这些问题。
英文摘要：
In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
