This paper proposes a K-means algorithm with the dynamic adjustable number of clusters.
The algorithm uses the improved Euclidean distance formula to calculate the distance …

Data clustering is an important data exploration technique with many applications in data
mining. The k-means algorithm is well known for its efficiency in clustering large data sets …

Aiming at the problems of the traditional K‐means clustering algorithm, such as the local
optimal solution and the slow clustering speed caused by the uncertainty of k value and the …

Clustering is a process used to divide data into a number of groups. All data points have
some mathematical parameter according to which grouping can be done. For instance, if we …

Abstract k-means is one of the most widely used partition based clustering algorithm. But the
initial centroids generated randomly by the k-means algorithm cause the algorithm to …

Clustering is the grouping together of similar data items into clusters. Clustering analysis is
one of the main analytical methods in data mining; the method of clustering algorithm will …

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a
clustering algorithm based on k-mean paradigm that works well for data with mixed numeric …