There is a need to scrutinise and retrieve information from data in today's world. Clustering an analytical technique which involves dividing into groups of similar objects. Every group called cluster, it formed objects that have affinities within the cluster but are significantly different other groups. The aim this paper look at compare two types hierarchical clustering algorithms. Partition ...

This paper deals with lidar point cloud filtering and classification for modelling the Terrain and more generally for scene segmentation. In this study, we propose to use the well-known K-means clustering algorithm that filters and segments (point cloud) data. The Kmeans clustering is well adapted to lidar data processing, since different feature attributes can be used depending on the desired ...

We present the results of exploratory data analysis for a data set that consists of crossposting information for 89,687 newsgroups over a period of 3.4 years. The data set we use is a part of Microsoft Netscan data. Our goal is to investigate the community structure of the newsgroup data set with a specific focus on spectral hierarchical clustering. We present a spectral hierarchical clustering...

Last time, we introduced the task of hierarchical clustering, in which we aim to produce nested clusterings that reflect the similarity between clusters. This contrasts sharply with our former discussion of “flat” or structureless clustering methods like k-means which do not model relationships between clusters. In this lecture, we will continue our discussion of the standard model-free approac...

This vignette shows the use of HCsnip package for extracting clusters from the Hierarchical Clustering (HC) tree in semi-supervised way. Rather than cutting the HC tree at a fixed highest (as existing methods do), it snips the tree at variable heights to extract hidden clusters. Cluster extraction process uses both the data matrix from which HC tree is derived and the available follow-up inform...