Algorithms for hierarchical clustering: an overview, II
نویسندگان
چکیده
منابع مشابه
Algorithms for hierarchical clustering: an overview
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clusterin...
متن کاملParallel Algorithms for Hierarchical Clustering
Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n*) algorithms are known for this problem [3,4,11,19]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distanc...
متن کاملAlgorithms for Model-Based Gaussian Hierarchical Clustering
Agglomerative hierarchical clustering methods based on Gaussian probability models have recently shown promise in a variety of applications. In this approach, a maximum-likelihood pair of clusters is chosen for merging at each stage. Unlike classical methods, model-based methods reduce to a recurrence relation only in the simplest case, which corresponds to the classical sum of squares method. ...
متن کاملRandomized Algorithms for Fast Bayesian Hierarchical Clustering
We present two new algorithms for fast Bayesian Hierarchical Clustering on large data sets. Bayesian Hierarchical Clustering (BHC) [1] is a method for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. BHC has several advantages over traditional distancebased agglomerative clustering algorithms. It defines a probabilistic model of the data a...
متن کاملEfficient Active Algorithms for Hierarchical Clustering
Advances in sensing technologies and the growth of the internet have resulted in an explosion in the size of modern datasets, while storage and processing power continue to lag behind. This motivates the need for algorithms that are efficient, both in terms of the number of measurements needed and running time. To combat the challenges associated with large datasets, we propose a general framew...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: WIREs Data Mining and Knowledge Discovery
سال: 2017
ISSN: 1942-4787,1942-4795
DOI: 10.1002/widm.1219