This work presents a neural network model for the clustering analysis of data based on Self Organizing Maps (SOM). The model evolves during the training stage towards a hierarchical structure according to the input requirements. The hierarchical structure symbolizes a specialization tool that provides refinements of the classification process. The structure behaves like a single map with differ...

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...

Consensus patterns, like motifs and tandem repeats, are highly conserved patterns with very few substitutions where no gaps are allowed. In this paper, we present a progressive hierarchical clustering technique for discovering consensus patterns in biological databases over a certain length range. This technique can discover consensus patterns with various requirements by applying a post-proces...

Clustering is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. The different partitioning methods studied here are k-means and k-medoids. The different hierarchical t...

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...

Conventional clustering means classifying the given data objects as exclusive subsets (clusters).That means we can discriminate clearly whether an object belongs to a cluster or not. However such a partition is insufficient to represent many real situations. Therefore a fuzzy clustering method is offered to construct clusters with uncertain boundaries and allows that one object belongs to overl...

Constrained clustering received a lot of attention in the last years. However, the widely used pairwise constraints are not generally applicable for hierarchical clustering, where the goal is to derive a cluster hierarchy instead of a flat partition. Therefore, we propose for the hierarchical setting—based on the ideas of pairwise constraints—the use of must-link-before (MLB) constraints. In th...

Abstract— Clustering is a powerful technique of data mining for extracting useful information from a set of data and classifies the data into several clusters based on similarity of the pattern. This paper presents the quality estimation for students’ projects data based on hierarchical clustering and fuzzy clustering using Min-Max method. From the experimental results it is seen the fuzzy clus...