A new model called Clustering with Neural Network and Index (CNNI) is introduced. CNNI uses a Neural Network to cluster data points. Training of the Neural Network mimics supervised learning, with an internal clustering evaluation index acting as the loss function. An experiment is conducted to test the feasibility of the new model, and compared with results of other clustering models like K-means and Gaussian Mixture Model (GMM). The result shows CNNI can work properly for clustering data; CNNI equipped with MMJ-SC, achieves the first parametric (inductive) clustering model that can deal with non-convex shaped (non-flat geometry) data
The ability to determine clusters or similarity in large, multivariate data sets is critical to many...
This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solv...
Cluster analysis plays an important role for understanding various phenomena and exploring the natur...
A new model called Clustering with Neural Network and Index (CNNI) is introduced. CNNI uses a Neural...
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a pro...
Neural Networks is known for its ability to derive the complicated data to extract the complex infor...
To classify objects based on their features and characteristics is one of the most important and pri...
The following full text is a publisher's version. For additional information about this publica...
In this paper, we propose a new clustering module that can be trained jointly with existing neural n...
Artificial neural networks are computational models inspired by neurobiology for enhancing and testi...
This paper presents a new growing neural network for cluster analysis and market segmentation, which...
This article presents a review of traditional and current methods of classification in the framework...
Decker R, Holsing C, Lerke S. Generating Normally Distributed Clusters by Means of a Self-organizing...
This thesis explores developing a Convolutional Neural Network (CNN) trained to assess clustering of...
Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms,...
The ability to determine clusters or similarity in large, multivariate data sets is critical to many...
This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solv...
Cluster analysis plays an important role for understanding various phenomena and exploring the natur...
A new model called Clustering with Neural Network and Index (CNNI) is introduced. CNNI uses a Neural...
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a pro...
Neural Networks is known for its ability to derive the complicated data to extract the complex infor...
To classify objects based on their features and characteristics is one of the most important and pri...
The following full text is a publisher's version. For additional information about this publica...
In this paper, we propose a new clustering module that can be trained jointly with existing neural n...
Artificial neural networks are computational models inspired by neurobiology for enhancing and testi...
This paper presents a new growing neural network for cluster analysis and market segmentation, which...
This article presents a review of traditional and current methods of classification in the framework...
Decker R, Holsing C, Lerke S. Generating Normally Distributed Clusters by Means of a Self-organizing...
This thesis explores developing a Convolutional Neural Network (CNN) trained to assess clustering of...
Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms,...
The ability to determine clusters or similarity in large, multivariate data sets is critical to many...
This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solv...
Cluster analysis plays an important role for understanding various phenomena and exploring the natur...