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Visualization and Interactive Feature Selection for Unsupervised Data (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Dy, Jennifer G. Brodley, Carla E. |
| Description | For many feature selection problems, a human denes the features that are potentially useful, and then a subset is chosen from the original pool of features using an automated feature selection algorithm. In contrast to supervised learning, class information is not available to guide the feature search for unsupervised learning tasks. In this paper, we introduce Visual-FSSEM (Visual Feature Subset Selection using Expectation-Maximization Clustering), which incorporates visualization techniques, clustering, and user interaction to guide the feature subset search and to enable a deeper understanding of the data. Visual-FSSEM, serves both as an exploratory and multivariate-data visualization tool. We illustrate Visual-FSSEM on a high-resolution computed tomography lung image data set. 1. INTRODUCTION Most research in unsupervised clustering assumes that when creating the target data set, the data analyst in conjunction with the domain expert was able to identify a small relevant set of ... |
| File Format | |
| Language | English |
| Publisher Date | 2000-01-01 |
| Publisher Institution | In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD |
| Access Restriction | Open |
| Subject Keyword | Expectation-maximization Clustering Original Pool Data Analyst Small Relevant Set Multivariate-data Visualization Tool Class Information Interactive Feature Selection Target Data Set Visualization Technique Unsupervised Clustering Assumes Tomography Lung Image Data Domain Expert Unsupervised Learning Task Visual Feature Subset Selection Many Feature Selection Problem Feature Search User Interaction Automated Feature Selection Algorithm |
| Content Type | Text |
| Resource Type | Article |
