“Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids
نویسندگان
چکیده
منابع مشابه
Interactive Hierarchical Clustering using Bayesian Nonparametrics
A widely-used class of algorithms to understand data is hierarchical clustering, but it is often difficult to reconcile the results of these algorithms with hierarchies constructed by humans. Interaction, or querying humans for constraints on the data, is a popular solution for addressing this discrepancy. In this paper, we propose using leave-one-out interactions to achieve better hierarchies ...
متن کاملBayesian Hierarchical Cross-Clustering
Most clustering algorithms assume that all dimensions of the data can be described by a single structure. Cross-clustering (or multiview clustering) allows multiple structures, each applying to a subset of the dimensions. We present a novel approach to crossclustering, based on approximating the solution to a Cross Dirichlet Process mixture (CDPM) model [Shafto et al., 2006, Mansinghka et al., ...
متن کاملInteractive Bayesian Hierarchical Clustering
Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user’s needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering...
متن کاملDynamic Networks from Hierarchical Bayesian Graph Clustering
Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying interactions into a single static map. Conventional algorithms are limited to analyzing evolving networks by reducing them to a series of unrelated ...
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 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: Information Sciences
سال: 2017
ISSN: 0020-0255
DOI: 10.1016/j.ins.2017.08.017