Clustering is a data analysis technique that, when applied to a set of heterogeneous items,
identifies homogeneous subgroups as defined by a given model or measure of similarity. Of …

Medical data is one of the most rewarding and yet most complicated data to analyze. How
can healthcare providers use modern data analytics tools and technologies to analyze and …

Clustering algorithms are attractive for the task of class identification in spatial databases.
However, the application to large spatial databases rises the following requirements for …

In this paper, we present a novel algorithm for performing k-means clustering. It organizes all
the patterns in a kd tree structure such that one can find all the patterns which are closest to …

We present a practical approach to nonparametric cluster analysis of large data sets. The
number of clusters and the cluster centres are automatically derived by mode seeking with …

The k-means algorithm is one of the most widely used methods to partition a dataset into
groups of patterns. However, most k-means methods require expensive distance …

Capturing analytical data from the fusion of medical images is a demand and emerging area
of exploration. A vast area of applications of image fusion demonstrates its importance in the …

Low level computer vision tasks are misleadingly difficult and can yield unreliable results,
since often the employed techniques rely upon inaccurate parametric models. This thesis …

Lidar-based 3D tree reconstruction enables the retrieval of detailed tree structure; however,
many existing methods are based on high-density discrete return lidar datasets. In this …

This paper investigates a new approach to data clustering. The probability density function
(pdf) is estimated by using the Parzen window technique. The pdf thresholding permits the …