改进的K-means聚类k值选择算法

空间聚类算法中,聚类的效果在很大程度上受制于最佳k值的选择。典型的K-均值算法中,聚类数k需要事先确定,但在实际情况中k的取值很难确定。针对手肘法在确定k值的过程中存在的"肘点"位置不明确问题,基于指数函数性质、权重调节、偏执项和手肘法基本思想,提出了一种改进的k值选择算法ET-SSE算法。通过多个UCI数据集和K-means聚类算法对该算法进行实验,结果表明,使用该k值选择算法相比于手肘法能更加快速且准确地确定k值。

Improved K-means Clustering k-Value Selection Algorithm

In spatial clustering algorithms, the effect of clustering depends to a large extent on the choice of the best [k] value. In the typical [K]-means algorithm, the [k] value of clusters needs to be determined in advance, but in actual cases, the value of [k] is difficult to determine. The paper proposes an improved [k]-value selection algorithm, ET-SSE, based on the nature of exponential function, weight adjustment, bias and Elbow Method for the “elbow-point” ambiguity in the process of determining the [k]-value. The algorithm is tested by multiple UCI data sets and [K]-means clustering algorithm. The results show that the [k]-value selection algorithm can determine the value of key more accurately than the Elbow Method.