Unsupervised multi-scale fuzzy clustering algorithm application in the evaluation of soil fertility
- 10.2991/iccia.2012.154How to use a DOI?
- Data mining, clustering algorithm, unsupervised, multi-scale
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.
- © 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/).
Cite this article
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 -