This paper shows a new combinatorial problem which emerged from studies on an artificial intelligence classification model of a hierarchical classifier. We introduce the notion of proper clustering and show how to count their number in a special case when 3 clusters are allowed. An algorithm that generates all clusterings is given. We also show that the proposed approach can be generalized to any number of clusters, and can be automatized. Finally, we show the relationship between the problem of counting clusterings and the Dedekind problem
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
textAnalysis of large collections of data has become inescapable in many areas of scientific and com...
AbstractA set X is said to properly intersect a set Y if none of the sets X∩Y, X⧹Y and Y⧹X is empty....
This paper shows a new combinatorial problem which emerged from studies on an arti cial intelligenc...
This paper shows a new combinatorial problem which emerged from studies on an artificial intelligenc...
International audienceAgglomerative clustering methods have been widely used by many research commun...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Clustering is one of the most popular artificial intelligence techniques which aims at identifying g...
Determining the number of clusters is one of the most important topics in cluster analysis. The abil...
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an ano...
Hierarchical clustering is one of the most suitable tools to discover the underlying true structure ...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mini...
Clustering is a central unsupervised learning task with a wide variety of applications. Unlike in su...
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
textAnalysis of large collections of data has become inescapable in many areas of scientific and com...
AbstractA set X is said to properly intersect a set Y if none of the sets X∩Y, X⧹Y and Y⧹X is empty....
This paper shows a new combinatorial problem which emerged from studies on an arti cial intelligenc...
This paper shows a new combinatorial problem which emerged from studies on an artificial intelligenc...
International audienceAgglomerative clustering methods have been widely used by many research commun...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Clustering is one of the most popular artificial intelligence techniques which aims at identifying g...
Determining the number of clusters is one of the most important topics in cluster analysis. The abil...
In this paper we make two novel contributions to hierarchical clustering. First, we introduce an ano...
Hierarchical clustering is one of the most suitable tools to discover the underlying true structure ...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
In this paper we present a method to detect natural groups in a data set, based on hierarchical clus...
Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mini...
Clustering is a central unsupervised learning task with a wide variety of applications. Unlike in su...
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
textAnalysis of large collections of data has become inescapable in many areas of scientific and com...
AbstractA set X is said to properly intersect a set Y if none of the sets X∩Y, X⧹Y and Y⧹X is empty....