By Topic

Utilizing Fused Features to Mine Unknown Clusters in Training Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lynch, R.S. ; Signal Process. Branch, Naval Undersea Warfare Center, Newport, RI ; Willett, P.K.

In this paper, a previously introduced data mining technique, utilizing the mean field Bayesian data reduction algorithm (BDRA), is extended for use in finding unknown data clusters in a fused multidimensional feature space. In the BDRA the modeling assumption is that the discrete symbol probabilities of each class are a priori uniformly Dirichlet distributed, and where the primary metric for selecting and discretizing all relevant features is an analytic formula for the probability of error conditioned on the training data. In extending the BDRA for this application, notice that its built-in dimensionality reduction aspects are exploited for isolating and automatically sorting out and mining all points contained in each unknown data cluster. To illustrate performance, results are demonstrated using simulated data containing multiple clusters, and where the fused feature space contains relevant classification information

Published in:

Information Fusion, 2006 9th International Conference on

Date of Conference:

10-13 July 2006