Witnessing the tremendous development of machine learning technology, emerging applications impose challenges using domain knowledge to improve accuracy clustering provided that suffers a compromising rate despite its advantage fast procession. In this paper, we model (i.e., background or side information), respecting some as must-link and cannot-link sets, for sake collaborating with k-means b...

COVID-19 hits the world like a storm by arising pandemic situations for most of countries around world. The whole is trying to overcome this situation. A better health care quality may help country tackle pandemic. Making clusters with similar types provides an insight into in different countries. In area machine learning and data science, K-means clustering algorithm typically used create base...

Clustering is one of the important data mining techniques. k-Means [1] is one of the most important algorithm for Clustering. Traditional k-Means algorithm selects initial centroids randomly and in k-Means algorithm result of clustering highly depends on selection of initial centroids. k-Means algorithm is sensitive to initial centroids so proper selection of initial centroids is necessary. Thi...

This article proposes a constrained clustering algorithmwith competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learn...

The k-means clustering algorithm, a staple of data mining and unsupervised learning, is popular because it is simple to implement, fast, easily parallelized, and offers intuitive results. Lloyd’s algorithm is the standard batch, hill-climbing approach for minimizing the k-means optimization criterion. It spends a vast majority of its time computing distances between each of the k cluster center...

Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns weights to the clusters relative to their variance and optimizes a weighted version of the k-Mean...