Cluster analysis is a popular method for data investigation where data items are structured into groups called clusters. This analysis involves two sequential steps, namely cluster formation and cluster evaluation. In this paper, we propose the tight integration of cluster formation and cluster evaluation in interactive visual analysis in or-der to overcome the challenges that relate to the black-box nature of clustering algorithms. We present our conceptual framework in the form of an interactive visual environment. In this realization of our framework, we build upon general concepts such as cluster com-parison, clustering tendency, cluster stability and cluster coherence. Additionally, we showcase our framework on the cluster analysis of ...
PurposeThe purpose of this paper is to provide a review of the issues related to cluster analysis, o...
Visualization is helpful for clustering high dimensional data. The goals of visualization in data mi...
The paper shows how two powerful techniques for supporting data exploration of multidimensional data...
AbstractIn this work the topic of applying clustering as a knowledge extraction method from real-wor...
Cluster analysis is a useful method which reveals underlying structures and relations of items after...
Abstract. A common issue in cluster analysis is that there is no single correct answer to the number...
Abstract Background With ever-increasing amounts of data produced in biology research, scientists ar...
Clustering is a major technique in data mining. However the numerical feedback of clustering algorit...
Abstract: The visual assessment of tendency (VAT) technique, developed by J.C. Bezdek, R.J. Hathaway...
We demonstrate interactive visual embedding of partition-based clustering of multidimensional data u...
Due to recent advances in information technology, there has been an enormous growth in the amount of...
Cluster analysis is a fundamental principle in exploratory data analysis, providing the user with a ...
Visual methods have been extensively studied and performed in cluster data analysis. Given a pairwis...
AbstractA new graphical display is proposed for partitioning techniques. Each cluster is represented...
We improve the visual assessment of tendency (VAT) technique, which, developed by J.C. Bezdek, R.J. ...
PurposeThe purpose of this paper is to provide a review of the issues related to cluster analysis, o...
Visualization is helpful for clustering high dimensional data. The goals of visualization in data mi...
The paper shows how two powerful techniques for supporting data exploration of multidimensional data...
AbstractIn this work the topic of applying clustering as a knowledge extraction method from real-wor...
Cluster analysis is a useful method which reveals underlying structures and relations of items after...
Abstract. A common issue in cluster analysis is that there is no single correct answer to the number...
Abstract Background With ever-increasing amounts of data produced in biology research, scientists ar...
Clustering is a major technique in data mining. However the numerical feedback of clustering algorit...
Abstract: The visual assessment of tendency (VAT) technique, developed by J.C. Bezdek, R.J. Hathaway...
We demonstrate interactive visual embedding of partition-based clustering of multidimensional data u...
Due to recent advances in information technology, there has been an enormous growth in the amount of...
Cluster analysis is a fundamental principle in exploratory data analysis, providing the user with a ...
Visual methods have been extensively studied and performed in cluster data analysis. Given a pairwis...
AbstractA new graphical display is proposed for partitioning techniques. Each cluster is represented...
We improve the visual assessment of tendency (VAT) technique, which, developed by J.C. Bezdek, R.J. ...
PurposeThe purpose of this paper is to provide a review of the issues related to cluster analysis, o...
Visualization is helpful for clustering high dimensional data. The goals of visualization in data mi...
The paper shows how two powerful techniques for supporting data exploration of multidimensional data...