Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters

K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for
its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters
and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user
must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm
with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for
effective and efficient clustering process. The underlined mechanism has been analyzed and experimented.
The experimental results show that the number of iteration is reduced to 50% and the run time is lower and
constantly based on maximum distance of data points, regardless of how many data points.