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Clustering and Classification via Cluster-Weighted Factor Analyzers
Cluster-weighted models factor analysis mixturemodels parsimonious models
2012/11/23
In model-based clustering and classification, the cluster-weighted model constitutes a convenient approach when the random vector of interest constitutes a response variable Y and a set p of explanato...
Correlated variables in regression: clustering and sparse estimation
Canonical correlation group Lasso Hierarchical clustering High-dimensional inference Lasso Oracle inequality Variable screening Variable selection
2012/11/23
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as the Lasso for cluste...
Interest Rate Manipulation Detection using Time Series Clustering Approach
Interest Rate Manipulation Detection Time Series Clustering Approach
2012/9/18
The Interbank Offered Rate is a vital benchmark interest rate in the financial markets of every country to which financial contracts are tied. In the light of the recent LIBOR manipulation incident, t...
Interest Rate Manipulation Detection using Time Series Clustering Approach
Interest Rate Manipulation Detection Time Series Clustering Approach
2012/9/18
The Interbank Offered Rate is a vital benchmark interest rate in the financial markets of every country to which financial contracts are tied. In the light of the recent LIBOR manipulation incident, t...
Semi-supervised Clustering Ensemble by Voting
clustering ensembles semi supervised clustering consensus function ensemble generation.
2012/9/18
Clustering ensemble is one of the most recent advances in unsupervised learning. It aims to combine the clustering results obtained using different algorithms or from different runs of ...
On computation of clustering coefficient in a class of random networks
random graph clustering degree of separation
2012/9/18
The random networks enriched with additional structures asmetric and group-symmetry in background metric space are investigated. The important quantities like he clustering coefficient as well as the ...
Ensemble Clustering with Logic Rules
ensemble learning clustering, biological annotation logic rule random projection
2012/9/19
In this article, the logic rule ensembles approach to supervised learning is applied to the unsupervised or semi-supervised clustering. Logic rules which were obtained by combining simple conjunctive ...
Fast Planar Correlation Clustering for Image Segmentation
Fast Planar Correlation Clustering Image Segmentation
2012/9/18
We describe a new optimization scheme for nding high-quality clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the op...
Clustering function: a measure of social influence
clustering coecient power law social network intersection graph
2012/9/19
A commonly used characteristic of statistical dependence of adjacency relations in real networks, the clustering coecient, evaluates chances that two neighbours of a given vertex are adjacent. Anothe...
Hierarchical Clustering using Randomly Selected Similarities
Hierarchical Clustering Randomly Selected Similarities
2012/9/19
The problem of hierarchical clustering items from pairwisesimilarities is found across various scientific disciplines, from biology to networking. Often, applications of clustering techniques are limi...
Model-Based Clustering of Large Networks
social networks stochastic block models finite mixture models EM algorithms generalized EM algorithms variational EM algorithms MM algorithms
2012/9/18
We describe a network clustering framework, based on finite mix-ture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge variables. Rela-ti...
Revealing spatial variability structures of geostatistical functional data via Dynamic Clustering
functional data clustering geostatistics variogram
2011/7/6
In several environmental applications data are functions of time, essentially con- tinuous, observed and recorded discretely, and spatially correlated. Most of the methods for analyzing such data are ...
Dynamic Large Spatial Covariance Matrix Estimation in Application to Semiparametric Model Construction via Variable Clustering: the SCE approach
Time Series Covariance Estimation Regularization, Sparsity
2011/7/6
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a...
Factorial clustering methods have been developed in recent years thanks to the improving of computational power. These methods perform a linear transformation of data and a clustering on transformed d...
Source Separation and Clustering of Phase-Locked Subspaces: Derivations and Proofs
Index Terms—phase-locking synchrony source separation
2011/7/5
Due to space limitations, our submission "Source Separation and Clustering of Phase-Locked Subspaces", accepted for publication on the IEEE Transactions on Neural Networks in 2011, presented some resu...