Approximating Hierarchical MV-sets for Hierarchical Clustering
The goal of hierarchical clustering is to construct a cluster tree, which can be viewed as the modal structure of a density. For this purpose, we use a convex optimization program that can efficiently estimate a family of hierarchical dense sets in high-dimensional distributions. We further extend existing graph-based methods to approximate the cluster tree of a distribution. By avoiding direct density estimation, our method is able to handle high-dimensional data more efficiently than existing density-based approaches. We present empirical results that demonstrate the superiority of our method over existing ones.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDensity EstimationSimilar Papers 제목 키워드 기반
Multi-rank Sparse Hierarchical Clustering
There has been a surge in the number of large and flat data sets - data sets containing a large number of features and a relatively small number of observations - due to the growing ability to collect and store informati…
Clusteringfeature selectionA Fast Quartet Tree Heuristic for Hierarchical Clustering
The Minimum Quartet Tree Cost problem is to construct an optimal weight tree from the $3{n \choose 4}$ weighted quartet topologies on $n$ objects, where optimality means that the summed weight of the embedded quartet top…
Clusteringglobal-optimizationA Novel Multi-clustering Method for Hierarchical Clusterings, Based on Boosting
Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering metho…
ClusteringAverage Sensitivity of Hierarchical $k$-Median Clustering
Hierarchical clustering is a widely used method for unsupervised learning with numerous applications. However, in the application of modern algorithms, the datasets studied are usually large and dynamic. If the hierarchi…
Contrastive Multi-view Hyperbolic Hierarchical Clustering
Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., mul…
Clustering