Adaptive Data Mining Algorithm under the Massive Data

DOI

10.2991/iset-15.2015.10How to use a DOI?

Keywords

Neural networks, Data mining, Clustering, Rule extraction

Abstract

In order to solve the problem that Network Reduced accuracy and poor convergence in the existing neural network, which because sample large volumes of data and target data-independent. In response to this phenomenon, this paper put forward a data mining based on compensatory fuzzy neural network. It was optimizing was the Compensative Fuzzy Neural Network. And improve the cutting effect base on calculation algorithm. At the end, it was based on the similarity of each cluster objects to clustering process the system data. Through simulation experiments we can see, algorithm can maintain high precision under different circumstances the amount of data. Compared to other algorithms, we can see that it has a large advantage in terms of both accuracy and time-consuming.

Copyright

© 2015, 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  - Weijian Mo
PY  - 2015/03
DA  - 2015/03
TI  - Adaptive Data Mining Algorithm under the Massive Data
BT  - Proceedings of the First International Conference on Information Science and Electronic Technology
PB  - Atlantis Press
SP  - 37
EP  - 40
SN  - 2352-538X
UR  - https://doi.org/10.2991/iset-15.2015.10
DO  - 10.2991/iset-15.2015.10
ID  - Mo2015/03
ER  -