A comparison of unsupervised neural networks and k-means clustering in the analysis of multi-element stream sediment data-Reference-Cited by-同舟云学术

A comparison of unsupervised neural networks and k-means clustering in the analysis of multi-element stream sediment data

Published:2001-05 Issue:2 Volume:1 Page:119-134
ISSN:1467-7873
Container-title:Geochemistry: Exploration, Environment, Analysis
language:en
Short-container-title:Geochemistry: Exploration, Environment, Analysis

Author:

Clare A. P.,Cohen D. R.

Publisher

Geological Society of London

Subject

General Earth and Planetary Sciences,Geochemistry and Petrology,General Environmental Science,General Chemistry

Reference22 articles.

1. BARNES, R. G. , BROWNLOW, J, ALDER, D. & 6 others 1996. Mineral resources. In: Regional Report of Upper North East New South Wales, Volume 5; Socio-economic Values. Resource and Conservation Assessment Council, Sydney, 73–154.

2. Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data

3. BONHAM-CARTER, G. F. 1994. Geographic Information Systems for Geoscientists: Modelling with GIS. Pergamon Publishing, Oxford.

4. A spatial analysis method for geochemical anomaly separation

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