Training of Artificial Neural Networksin Data Mining
K. Raja Sekhar1, V. Srinivasa Kalyan2, B. Phanindra Kumar3
1K.Raja Sekhar, Assistant Professor, Department of CSE, KL University, Guntur (Andhra Pradesh), India.
2V.Srinivasa Kalyan, Assistant Professor, Department of CSE, KL University, Guntur (Andhra Pradesh), India.
3B.Phanindra Kumar, Assistant Professor, Department of CSE, KL University, Guntur (Andhra Pradesh), India.
Manuscript received on 10 July 2013 | Revised Manuscript received on 18 July 2013 | Manuscript Published on 30 July 2013 | PP: 214-217 | Volume-3 Issue-2, July 2013 | Retrieval Number: B1028073213/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Companies have been collecting data for decades, building massive data warehouses in which to store it. Even though this data is available, very few companies have been able to realize the actual value stored in it. The question these companies are asking is how to extract this value. The answer is Data mining. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Many practitioners are wary of Neural Networks due to their black box nature, even though they have proven themselves in many situations. This paper is an overview of artificial neural networks and questions their position as a preferred tool by data mining practitioners.
Keywords: Artificial Neural Network (ANN), Neural Network Topology, Data Mining, Back Propagation Algorithm, Advantages.
Scope of the Article: Data Mining