Unsupervised multi-scale fuzzy clustering algorithm application in the evaluation of soil fertility

DOI

10.2991/iccia.2012.154How to use a DOI?

Keywords

Data mining, clustering algorithm, unsupervised, multi-scale

Abstract

this paper adopts statistical learning theory and optimization theory to the analysis of the algorithm theory, probe into its theoretical foundation. The existing theoretical analysis on the basis of the establishment of clustering model algorithm design, code realization and finally a lot of different data set of test, choose soil data as a test database, will be in the database on a large number of data mining experiment to verify the performance of the proposed algorithm. The test result feedback back will further deepen the theoretical research or correct theory already mistakes, new theory and will continue to guide experiments, both mutual promoting common development.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liying Cao
AU  - Helong Yu
AU  - Li Ma
AU  - Guifen Chen
PY  - 2014/05
DA  - 2014/05
TI  - Unsupervised multi-scale fuzzy clustering algorithm application in the evaluation of soil fertility
BT  - Proceedings of the 2012 2nd International Conference on Computer and Information Application (ICCIA 2012)
PB  - Atlantis Press
SP  - 638
EP  - 641
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccia.2012.154
DO  - 10.2991/iccia.2012.154
ID  - Cao2014/05
ER  -