Pregnancy Leave Wages Compensation based on Data Mining: Clients of Iran’s Social Security Organization
Amir Rajaei1, Mahshid Sedighi2

1Amir Rajaei*, Department of Computer Engineering, Velayat University, Iranshahr, Iran.
2Mahshid Sedighie, Department of Computer Engineering, Velayat University, Iranshahr, Iran

Manuscript received on October 11, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 782-794 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4286119119/2019©BEIESP | DOI: 10.35940/ijitee.A4286.119119
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Abstract: Data mining is an interdisciplinary science which exploits different methods including statistics, pattern recognition, machine learning, and database to extract the knowledge hidden in huge datasets. In this paper, we sought to develop a model for paying pregnancy period wages compensation to the Social Security Organization (SSO) clients by using data mining techniques. The SSO is a public insurance organization, the main mission of which is to cover the stipendiary workers (mandatory) and self-employed people (optional). In order to develop the proposed model, 5931 samples were selected randomly from 11504 clients. Then the K-Means clustering algorithm was employed to divide data into cluster 1, consisting of 2732 samples, and cluster 2, consisting of 3199 samples. In each cluster, the data were divided into training and test sets with a ratio of 90 to 10. Then a multi-layer perceptron neural network was trained separately for each cluster. This paper utilized the MLP network model. The tanh transfer function was used as the activation function in the hidden activation layer. Numerous tests were conducted to develop the best neural network structure with the lowest error rate. It consisted of two hidden layers. There were 5 neurons in the first layer and 4 neurons in the second. Therefore, the neural network structure was in the 5-4-1 format. Finally, the best model was selected by using the error evaluation method. The MAPE and R2 criteria were employed to evaluate the proposed model. Regarding the test data, the result was 0.96 for cluster 1 and 0.95 for cluster 2. The proposed method produced a lower error rate than the other existing models.
Keywords: K-Means Clustering, Particle Swarm Optimization, Neural Network, Social Security Organization, Data Mining
Scope of the Article: Clustering