改进K均值算法，让聚类更紧密并识别异常点
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm
- 通过迭代降低簇内方差，实现更紧凑的聚类结构
- 在葡萄酒质量数据集上方差减少88.1%，准确率提升22.5%
- 适合需要高精度聚类与异常检测的工业数据分析场景
本文提出一种统一的聚类优化与异常检测方法。新算法通过迭代减小N个簇的簇内方差，直至达到全局最小值，获得比标准K均值更紧凑的聚类结果。采用轮廓系数、Calinski-Harabasz指数和Davies-Bouldin指数等内在指标评估无监督学习性能，并通过识别导致方差显著增加的点实现异常检测。在合成数据及UCI乳腺癌、UCI葡萄酒质量数据集上进行外部验证，使用杰卡德相似度、V-measure和F1分数评估。结果表明，在合成数据集和葡萄酒质量数据集上分别实现18.7%和88.1%的方差降低，葡萄酒质量数据集上的准确率与F1分数分别提升22.5%和20.8%。
原文摘要 · Abstract (English)
This paper introduces a unified approach to cluster refinement and anomaly detection in datasets. We propose a novel algorithm that iteratively reduces the intra-cluster variance of N clusters until a global minimum is reached, yielding tighter clusters than the standard k-means algorithm. We evaluate the method using intrinsic measures for unsupervised learning, including the silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin index, and extend it to anomaly detection by identifying points whose assignment causes a significant variance increase. External validation on synthetic data and the UCI Breast Cancer and UCI Wine Quality datasets employs the Jaccard similarity score, V-measure, and F1 score. Results show variance reductions of 18.7% and 88.1% on the synthetic and Wine Quality datasets, respectively, along with accuracy and F1 score improvements of 22.5% and 20.8% on the Wine Quality dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。