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Cluster hypothesis

Known as: Cluster assumption 
In machine learning and information retrieval, the cluster hypothesis is an assumption about the nature of the data handled in those fields, which… 
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Related topics

Related topics

7 relations
Cluster analysisInformation retrievalK-means clusteringMachine learning

Broader (1)

Data modeling

Papers overview

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2019
2019
Learning the $k$ in $k$-means via the Camp-Meidell Inequality
Determining <tex>$k$</tex>, the number clusters, is a key challenge in <tex>$k$</tex>-means clustering. We propose an approach… 
2019
2019
Penalized k-means algorithms for finding the correct number of clusters in a dataset
In many applications we want to find the number of clusters in a dataset. A common approach is to use the penalized k-means… 
2017
2017
Semi-Supervised Classification based on Positive-Unlabeled Classification
In real-world machine learning tasks, the size of labeled data is often limited due to laborious manual annotation. In contrast… 
2010
2010
Semi-supervised Kernel Based Progressive SVM
Most existing semi-supervised methods implemented either the cluster assumption or the manifold assumption. The performance will… 
2010
2010
Multimodal kernel learning for image retrieval
We propose a semi-supervised learning technique to address the problem of fusing multimodal information sources for CBIR. In our… 
2010
2010
An Optimization Model for Fuzzy Binary Clustering
Clustering has been a powerful tool to visualize complex data with extensive applica- tions in many disciplines. In this paper… 
2008
2008
Binary Classification under Sample Selection Bias
1 Binary classification under sample selection bias 9 1. 
2008
2008
Hierarchical cluster kernels for supervised and semi-supervised learning
Semi-supervised learning became an important subdomain of machine learning in the last years. These methods try to exploit the… 
2008
2008
On Discriminative Semi-Supervised Classification
The recent years have witnessed a surge of interests in semi-supervised learning methods. A common strategy for these algorithms… 
2006
2006
Advanced Semisupervised SVM Approaches to Classification of Hyperspectral Data
In this paper, we propose two novel semisupervised algorithms based on Support Vector Machine (SVM) for classifi- cation of…