%0 Journal Article
%A WANG Jianren
%A MA Xin
%A DUAN Ganglong
%T Improved K-means Clustering k-Value Selection Algorithm
%D 2019
%R 10.3778/j.issn.1002-8331.1810-0075
%J Computer Engineering and Applications
%P 27-33
%V 55
%N 8
%X 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.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1810-0075