This work proposes an algorithm that uses paths based on tile segmentation to build complex clusters. After allocating data items (points) to geometric shapes in tile format, the complexity of our algorithm is related to the number of tiles instead of the number of points. The main novelty is the way our algorithm goes through the grids, saving time and providing good results. It does not demand any configuration parameters from users, making easier to use than other strategies. Besides, the algorithm does not create overlapping clusters, which simplifies the interpretation of results
International audienceWe present a new clustering algorithm by proposing a convex relaxation of hier...
Data Clustering is one of the most important issues in data mining and machine learning. Clustering ...
This paper contains a proposal to assign points to clusters, represented by their centers, based on ...
Clustering is a technique for the analysis of datasets obtained by empirical studies in several disc...
Data clustering is a difficult and challenging task, especially when the hidden clusters are of diff...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
Clustering methods are particularly well-suited for identifying classes in spatial databases. Howeve...
Abstract. Representative-based clustering algorithms are quite popular due to their relative high sp...
We study an agglomerative clustering problem motivated by visualizing disjoint glyphs (represented ...
Abstract Data clustering is a process of putting similar data into groups. Point-based clustering ag...
In this paper, a novel clustering algorithm is proposed to address the clustering problem within bot...
Representative-based clustering algorithms are quite popular due to their relative high speed and be...
Many spatial analyses involve constructing possibly non-convex polygons, also called "footprint...
In this article we propose a new distance-based clustering algorithm. Distance-based clustering meth...
International audienceWe present a new clustering algorithm by proposing a convex relaxation of hier...
Data Clustering is one of the most important issues in data mining and machine learning. Clustering ...
This paper contains a proposal to assign points to clusters, represented by their centers, based on ...
Clustering is a technique for the analysis of datasets obtained by empirical studies in several disc...
Data clustering is a difficult and challenging task, especially when the hidden clusters are of diff...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
Density-based and grid-based clustering are two main clustering approaches. The former is famous for...
Clustering methods are particularly well-suited for identifying classes in spatial databases. Howeve...
Abstract. Representative-based clustering algorithms are quite popular due to their relative high sp...
We study an agglomerative clustering problem motivated by visualizing disjoint glyphs (represented ...
Abstract Data clustering is a process of putting similar data into groups. Point-based clustering ag...
In this paper, a novel clustering algorithm is proposed to address the clustering problem within bot...
Representative-based clustering algorithms are quite popular due to their relative high speed and be...
Many spatial analyses involve constructing possibly non-convex polygons, also called "footprint...
In this article we propose a new distance-based clustering algorithm. Distance-based clustering meth...
International audienceWe present a new clustering algorithm by proposing a convex relaxation of hier...
Data Clustering is one of the most important issues in data mining and machine learning. Clustering ...
This paper contains a proposal to assign points to clusters, represented by their centers, based on ...