The main purpose of this paper is to suggest a family of Minkowski distances as a tool for measuring the stability of clusters yielded by a Cluster Analysis. Preliminarly, we recall the main properties of the Minkowski distances family and we introduce the Minkowski ultrametric distances. Latter a proof of the Theorem on the subdominant Minkowski ultrametric distances is given. Since the ultrametric distance between two units decreases whenever the Minkowski parameter increases, we suggest to evaluate the clusters stability by the identification of the Minkowski parameter that measures the convergence to stable clusters. The validity of the methodology is confirmed by an example of Cluster Analysis on a set of real data. This methodology ...
In this paper, we investigate stability-based methods for cluster model selection, in particular to ...
Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their...
This paper represents another step in overcoming a drawback of K-Means, its lack of defense against ...
The main purpose of this paper is to suggest a family of Minkowski distances as a tool for measuring...
The paper focuses on the problem to find an ultrametric whose distortion is close to optimal. We int...
This paper focuses on the problem to find an ultrametric whose distortion is close to optimal. We in...
An important problem in the application of cluster analysis is the decision regarding how many clust...
There are many distance-based methods for classification and clustering, and for data with a high n...
Among the areas of data and text mining which are employed today in OR, science, economy and technol...
Stability is a common tool to verify the validity of sample based algorithms. In clustering it is wi...
Stability is a common tool to verify the validity of sample based algorithms. In clustering it is wi...
This paper represents another step in overcoming a drawback of K-Means, its lack of defense against ...
The Minkowski weighted K-means (MWK-means) is a recently developed clustering algorithm capable of c...
In this paper we introduce the Constrained Minkowski Weighted K-Means. This algorithm calculates clu...
Clustering is a central topic in unsupervised learning and has a wide variety of applications. Howev...
In this paper, we investigate stability-based methods for cluster model selection, in particular to ...
Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their...
This paper represents another step in overcoming a drawback of K-Means, its lack of defense against ...
The main purpose of this paper is to suggest a family of Minkowski distances as a tool for measuring...
The paper focuses on the problem to find an ultrametric whose distortion is close to optimal. We int...
This paper focuses on the problem to find an ultrametric whose distortion is close to optimal. We in...
An important problem in the application of cluster analysis is the decision regarding how many clust...
There are many distance-based methods for classification and clustering, and for data with a high n...
Among the areas of data and text mining which are employed today in OR, science, economy and technol...
Stability is a common tool to verify the validity of sample based algorithms. In clustering it is wi...
Stability is a common tool to verify the validity of sample based algorithms. In clustering it is wi...
This paper represents another step in overcoming a drawback of K-Means, its lack of defense against ...
The Minkowski weighted K-means (MWK-means) is a recently developed clustering algorithm capable of c...
In this paper we introduce the Constrained Minkowski Weighted K-Means. This algorithm calculates clu...
Clustering is a central topic in unsupervised learning and has a wide variety of applications. Howev...
In this paper, we investigate stability-based methods for cluster model selection, in particular to ...
Recently, a three-stage version of K-Means has been introduced, at which not only clusters and their...
This paper represents another step in overcoming a drawback of K-Means, its lack of defense against ...