Extended K-Means Algorithm
Faliu Yi, Inkyu Moon · 2013
In the conventional K-means algorithm, the input data are automatically grouped into corresponding cluster by minimizing the within-cluster sum of squares. However, the traditional K-means algorithm doesn't do any constraints to the number of elements in each group. In the area of logistics management, each cluster will need to satisfy with a predefined number of elements. Thus, the clustering algorithm with controlled number of elements in each group is necessary. In this paper, we present a new method called extended k-means algorithm to extend the ordinary K-means approach. In this approach, the number of element in each group is adjusted by using greedy algorithm and the experimental results show that this extended K-means algorithm can work well for grouping data where the numbers of elements in each group need to be restrained.