This work introduces an alternative representation for large dimensional data sets. Instead of using 2D or 3D representations, data is located on the surface of a sphere. Together with this representation, a hierarchical clustering algorithm is defined to analyse and extract the structure of the data. The algorithm builds a hierarchical structure (a dendrogram) in such a way that different cuts of the structure lead to different partitions of the surface of the sphere. This can be seen as a set of concentric spheres, each one being of different granularity. Also, to obtain an initial assignment of the data on the surface of the sphere, a method based on Sammon's mapping has been developed.Peer Reviewe
In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propo...
Abstract. In hierarchical cluster analysis, dendrograms are used to visualize how clusters are forme...
International audienceIn a Euclidean ascending hierarchical clustering (AHC, Ward's method), the usu...
A cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wi...
This thesis introduces n-sphere clustering, a new method of cluster analysis, akin to agglomerative ...
Hierarchical clustering algorithms are typically more effective in detecting the true clustering str...
Abstract — In this paper, we re-consider the problem of mapping a high-dimensional data set into a l...
Given a polygonal model of some geometric object, a continuous level of detail system should facili...
Clustering partitions a dataset such that observations placed together in a group are similar but di...
Hierarchical Clustering. Hierarchical clustering methods construct a dendro-gram of the input datase...
The vertical axis of the dendrogram represents the distance or dissimilarity between labeled cluster...
The objective of data mining is to take out information from large amounts of data and convert it in...
Abstract — Clustering techniques have a wide use and importance nowadays. This importance tends to i...
Clustering aims to differentiate objects from different groups (clusters) by similarities or distanc...
Abstract- This paper presents a new iterative algorithm for automatically generating a hierarchical ...
In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propo...
Abstract. In hierarchical cluster analysis, dendrograms are used to visualize how clusters are forme...
International audienceIn a Euclidean ascending hierarchical clustering (AHC, Ward's method), the usu...
A cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wi...
This thesis introduces n-sphere clustering, a new method of cluster analysis, akin to agglomerative ...
Hierarchical clustering algorithms are typically more effective in detecting the true clustering str...
Abstract — In this paper, we re-consider the problem of mapping a high-dimensional data set into a l...
Given a polygonal model of some geometric object, a continuous level of detail system should facili...
Clustering partitions a dataset such that observations placed together in a group are similar but di...
Hierarchical Clustering. Hierarchical clustering methods construct a dendro-gram of the input datase...
The vertical axis of the dendrogram represents the distance or dissimilarity between labeled cluster...
The objective of data mining is to take out information from large amounts of data and convert it in...
Abstract — Clustering techniques have a wide use and importance nowadays. This importance tends to i...
Clustering aims to differentiate objects from different groups (clusters) by similarities or distanc...
Abstract- This paper presents a new iterative algorithm for automatically generating a hierarchical ...
In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propo...
Abstract. In hierarchical cluster analysis, dendrograms are used to visualize how clusters are forme...
International audienceIn a Euclidean ascending hierarchical clustering (AHC, Ward's method), the usu...