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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...
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...
Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables
EM algorithm High-dimension but low-sample size L1 penalization Microarray gene expression Mixture model Penalized likelihood
2009/9/16
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise...