Add to ORKGThe general aim of cluster analysis is to build prototypes, or typologies of units that present similar characteristics. In this paper we propose an alternative approach based on consensus analysis of two different clustering methods to suitably obtain proto- types. The clustering methods used are fuzzy c-means (centre approach) and archetypal analysis (extreme approach). The consensus clustering is used to assess the correspon- dence between the clustering solutions obtained
Clustering is a fundamental machine learning application, which partitions data into homogeneous gro...
ConsensusClusterPlus is a tool for unsupervised class discovery. This docu-ment provides a tutorial ...
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process t...
Usually, the aim of cluster analysis is to build prototypes, i.e., typologies of units that present ...
Usually, the aim of cluster analysis is to build prototypes, i.e., typologies of units that present ...
Clustering aims at classifying the unlabeled points in a data set into different groups or clusters,...
In the presented work two variants of the fuzzy clustering approach dedicated for determining the an...
A well known issue with prototype-based clustering is the user's obligation to know the right number...
Prototypes, as Rosch (1973) defined the term in the cognitive sciences field, are ideal exemplars th...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
Abstract. Cognitive psychology works have shown that the cognitive representa-tion of categories is ...
Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern re...
3We propose a tool for exploring the number of clusters based on pivotal methods and consensus clust...
Two extensions to the objective function-based fuzzyclustering are proposed. First, the (point) prot...
olfa.nasraoui_AT_louisville.edu One major limitation of many classical clustering algorithms is that...
Clustering is a fundamental machine learning application, which partitions data into homogeneous gro...
ConsensusClusterPlus is a tool for unsupervised class discovery. This docu-ment provides a tutorial ...
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process t...
Usually, the aim of cluster analysis is to build prototypes, i.e., typologies of units that present ...
Usually, the aim of cluster analysis is to build prototypes, i.e., typologies of units that present ...
Clustering aims at classifying the unlabeled points in a data set into different groups or clusters,...
In the presented work two variants of the fuzzy clustering approach dedicated for determining the an...
A well known issue with prototype-based clustering is the user's obligation to know the right number...
Prototypes, as Rosch (1973) defined the term in the cognitive sciences field, are ideal exemplars th...
Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clu...
Abstract. Cognitive psychology works have shown that the cognitive representa-tion of categories is ...
Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern re...
3We propose a tool for exploring the number of clusters based on pivotal methods and consensus clust...
Two extensions to the objective function-based fuzzyclustering are proposed. First, the (point) prot...
olfa.nasraoui_AT_louisville.edu One major limitation of many classical clustering algorithms is that...
Clustering is a fundamental machine learning application, which partitions data into homogeneous gro...
ConsensusClusterPlus is a tool for unsupervised class discovery. This docu-ment provides a tutorial ...
Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process t...