Optimization Of K-means Algorithm Research Articles

Interpretation and optimization of the k-means algorithm

The paper gives a new interpretation and a possible optimization of the wellknown k-means algorithm for searching for a locally optimal partition of the set A = {ai ∈ ℝn: i = 1, …, m} which consists of k disjoint nonempty subsets π1, …, πk, 1 ⩽ k ⩽ m. For this purpose, a new divided k-means algorithm was constructed as a limit case of the known smoothed k-means algorithm. It is shown that the algorithm constructed in this way coincides with the k-means algorithm if during the iterative procedure no data points appear in the Voronoi diagram. If in the partition obtained by applying the divided k-means algorithm there are data points lying in the Voronoi diagram, it is shown that the obtained result can be improved further.

An Improved K-Means Algorithm and its Application in Customer Classification of Network Enterprises

K-means algorithm has powerful ability to cluster large data sets due to its high efficiency in data mining but its calculation instability limits the application of the algorithm, so the research of intelligent optimization of K-means algorithm has become a hot research field for the researchers related. First the calculation instability of the original K-means algorithm is analyzed with more details; Second, the improvement of cluster seed selection methods and the calculation flow of K-means algorithm are redesigned to speed up the calculation and enhance the stability of the improved model; Third, the paper realizes and conducts the analysis in customer classification practice of the improved algorithm which show that the improved K-means algorithm has better performance in classification accuracy and calculation stability and can be used in customer classification for network trade enterprises practically.