Adaptive Data Mining Algorithm under the Massive Data
- Weijian Mo
- Corresponding Author
- Weijian Mo
Available Online March 2015.
- https://doi.org/10.2991/iset-15.2015.10How to use a DOI?
- Neural networks, Data mining, Clustering, Rule extraction
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Weijian Mo PY - 2015/03 DA - 2015/03 TI - Adaptive Data Mining Algorithm under the Massive Data BT - First International Conference on Information Science and Electronic Technology (ISET 2015) PB - Atlantis Press UR - https://doi.org/10.2991/iset-15.2015.10 DO - https://doi.org/10.2991/iset-15.2015.10 ID - Mo2015/03 ER -