In this paper we show that diversity-driven widening, the parallel exploration of the model space with focus on developing diverse models, can improve hierarchical agglomerative clustering. Depending on the selected linkage method, the model that is found through the widened search achieves a better silhouette coefficient than its sequentially built counterpart.publishe
When using a greedy algorithm for finding a model, as is the case in many data mining algorithms, th...
Hierarchical clustering is of great importance in data analytics especially because of the exponenti...
There are many clustering methods, such as hierarchical clustering method. Most of the approaches to...
We present two results which arise from a model-based approach to hierarchical agglomerative cluster...
We present two results which arise from a model-based approach to hierarchical agglomerative clust...
Current methods for hierarchical clustering of data either operate on features of the data or make l...
This paper follows our earlier publication, where we introduced the idea of tuned data mining which ...
Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well...
A semi-supervised agglomerative hierarchical clustering method based on dynamically updating constra...
Abstract. Agglomerative hierarchical clustering methods based on Gaussian probability models have re...
Hierarchical Agglomerative Classification (HAC) with Ward’s linkage has been widely used since its i...
Standard agglomerative clustering suggests establishing a new reliable linkage at every step. Howeve...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
Agglomerative hierarchical clustering methods based on Gaussian probability models have recently sho...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
When using a greedy algorithm for finding a model, as is the case in many data mining algorithms, th...
Hierarchical clustering is of great importance in data analytics especially because of the exponenti...
There are many clustering methods, such as hierarchical clustering method. Most of the approaches to...
We present two results which arise from a model-based approach to hierarchical agglomerative cluster...
We present two results which arise from a model-based approach to hierarchical agglomerative clust...
Current methods for hierarchical clustering of data either operate on features of the data or make l...
This paper follows our earlier publication, where we introduced the idea of tuned data mining which ...
Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well...
A semi-supervised agglomerative hierarchical clustering method based on dynamically updating constra...
Abstract. Agglomerative hierarchical clustering methods based on Gaussian probability models have re...
Hierarchical Agglomerative Classification (HAC) with Ward’s linkage has been widely used since its i...
Standard agglomerative clustering suggests establishing a new reliable linkage at every step. Howeve...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
Agglomerative hierarchical clustering methods based on Gaussian probability models have recently sho...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
When using a greedy algorithm for finding a model, as is the case in many data mining algorithms, th...
Hierarchical clustering is of great importance in data analytics especially because of the exponenti...
There are many clustering methods, such as hierarchical clustering method. Most of the approaches to...