In data mining, K-means is a simple and fast algorithm for solving clustering problems, but it
requires that the user provides in advance the exact number of clusters (k), which is often not …

K-Means clustering algorithm attracts increasing focus in recent years. A pending problem of
K-Means clustering algorithm is that the performance is affected by the original cluster …

For k-means algorithm to the initial cluster centers, sensitive to outliers shortcomings, we
propose a density-based improved k-means algorithm. The algorithm introduces entropy …

ABSTRACT K-means clustering algorithm is one of the best known algorithms used in
clustering; nevertheless it has many disadvantages as it may converge to a local optimum …

An optimized initial center of K-means algorithm (PKM) is proposed, which select the k
furthest distance data in the high-density area as the initial cluster centers. Experiments …

Being a measurable criterion of clustering quality for the classical K-means algorithm, the
objective function always exists many local minimum values. The objective function may …

Clustering is a data mining (machine learning), unsupervised learning technique used to
place data elements into related groups without advance knowledge of the group definitions …

Abstract k-Means clustering algorithm is a heuristic algorithm that partitions the dataset into k
clusters by minimizing the sum of squared distance in each cluster. In contrast, there are …