The K-means algorithm is widely used because of its reliable theory, simple algorithm, fast
convergence and it can effectively handle large data sets. However, the traditional K-means …

Traditional K-means algorithm is sensitive to the initial cluster centers, cluster results
fluctuate with different initial input and are easy to fall into local optimum. This paper …

K-means algorithm is commonly used in clustering algorithms to find clusters due to its
simplicity of implementation and fast execution. However, classical K-means algorithm is …

The traditional k-means algorithm has sensitivity to the initial start center. To solve this
problem, this paper proposed a new method to find the initial center and improve the …

Clustering analysis method is one of the main analytical methods in data mining, the method
of clustering algorithm will influence the clustering results directly. This paper discusses the …

K-means algorithm is one of the most popular clustering algorithms. However, it is sensitive
to initialized partition and the circular dataset. To attack this problem, this paper introduced …

K-means clustering algorithm and one of its enhancements are studied in this paper.
Clustering is the classification of objects into different groups, or more precisely, the …

The basic K-means is sensitive to the initial centre and easy to get stuck at local optimal
value. To solve such problems, a new clustering algorithm is proposed based on simulated …

As a classic clustering method, the traditional K-Means algorithm has been widely used in
pattern recognition and machine learning. It is known that the performance of the K-means …

K-means in the field of clustering analysis algorithms is a kind of more traditional algorithm. It
exists many shortcomings. For example, K value is easily affected by man-made subjective …