Data Transformation Techniques for Academic Datasets
V.Sathya Durga1, Thangakumar Jeyaprakash2
1V.Sathya Durga, Research Scholar, Department of CSE, Hindustan Institute of Technology and Science, Padur, India.
2Thangakumar Jeyaprakash, Associate Professor, Department of CSE, Hindustan Institute of Technology and Science, Padur, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2214-2218 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9711109119/2019©BEIESP | DOI: 10.35940/ijeat.A9711.109119
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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: Data mining is a real-world procedure of discovering useful patterns from heterogeneous datasets. All most all industry uses data mining in their day to day activities. To build an effective mining model, a series of development steps are to be followed. It starts with discovering the business problem and ends with communicating the results. In this development life cycle, the most important step is data preparation or data preprocessing. Data preprocessing is converting raw data into data understandable by the machine. Data normalization is a phase in data preprocessing where the data values are scaled to 0 and 1. Right normalization of the datasets leads to improved mining results. In this paper, academic data of students is taken. The dataset is normalization using six normalization technique. Multi Layer Perceptron classifier is applied to normalized dataset and results are obtained. Results of this study reveal the best normalization technique which can be used for normalizing academic datasets. Finally, in a line, the goal of this work is to discover the best normalization technique which produces better mining result when applied to academic datasets.
Keywords: Cube Root Normalization; Data Normalization; Decimal Scaling Normalization; Root Mean Square Error.