关键词: K-均值算法, k值选择, ET-SSE算法

Abstract: 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.

Key words: K-means algorithm, k-value selection, ET-SSE algorithm