Add to ORKGIn this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementation ally simple, and learn a different set of feature weights for each identified cluster. The cluster dependent feature weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in the subsequent steps of a learning system to improve its learning behavior. An extension of the algorithm to deal with an unknown number of clusters is also proposed. The extension is based on competitive agglomeration, whereby the number of clusters is over specified, and adjac...
High-dimensional data are becoming increasingly pervasive, and bring new problems and opportunities ...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
We present a discriminative clustering approach in which the feature representation can be learned f...
Clustering aims at classifying the unlabeled points in a data set into different groups or clusters,...
Fast and eective unsupervised clustering is a fundamental tool in unsupervised learning. Here is a n...
In the cluster analysis most of the existing clustering techniques for clustering, accept the number...
We introduce a novel algorithm for factorial learning, motivated by segmentation problems in computa...
Learning Classifier Systems (LCS) are a well-known machine learning method, producing sets of interp...
Similarity-based clustering is a simple but powerful technique which usually results in a clustering...
Unsupervised and semi-supervised learning are explored in convex clustering with metric learning whi...
Efficient partitioning of large data sets into homogenous clusters is a fundamental problem in data ...
Clustering is used in identifying groups of samples with similar properties, and it is one of the mo...
Abstract—Clustering in the neural-network literature is gener-ally based on the competitive learning...
Unsupervised image clustering is a chicken-and-egg problem that involves representation learning and...
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how wit...
High-dimensional data are becoming increasingly pervasive, and bring new problems and opportunities ...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
We present a discriminative clustering approach in which the feature representation can be learned f...
Clustering aims at classifying the unlabeled points in a data set into different groups or clusters,...
Fast and eective unsupervised clustering is a fundamental tool in unsupervised learning. Here is a n...
In the cluster analysis most of the existing clustering techniques for clustering, accept the number...
We introduce a novel algorithm for factorial learning, motivated by segmentation problems in computa...
Learning Classifier Systems (LCS) are a well-known machine learning method, producing sets of interp...
Similarity-based clustering is a simple but powerful technique which usually results in a clustering...
Unsupervised and semi-supervised learning are explored in convex clustering with metric learning whi...
Efficient partitioning of large data sets into homogenous clusters is a fundamental problem in data ...
Clustering is used in identifying groups of samples with similar properties, and it is one of the mo...
Abstract—Clustering in the neural-network literature is gener-ally based on the competitive learning...
Unsupervised image clustering is a chicken-and-egg problem that involves representation learning and...
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how wit...
High-dimensional data are becoming increasingly pervasive, and bring new problems and opportunities ...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
We present a discriminative clustering approach in which the feature representation can be learned f...