The Approach of Data Mining

A Performance-based Perspective of Segregated Data Estimation to Classify Distinction by Applying Diverse Data Mining Classifiers

  • Altaf Hussain
    Qatar University
  • Tariq Hussain
    Qatar University uom.tariq[at]
  • Ijaz Ullah
    University of Rennes 1


The concept of data mining is to classify and analyze the given data and to examine it clearly understandable and discoverable for the learners and researchers. The different types of classifiers are there exist to classify a data accordingly for the best and accurate results. Taking a primary data, and then classifying it into different portions of parts, then to analyze and remove any ambiguities from it and finally make it possible for understanding. With this process, that data will become secondary from primary and will called information. So, the classifiers are doing the same strategy for the solution and accuracy of the data. In this paper, different data mining approaches have been used by applying different classifiers on the taken data set. The data-set consists of 500 candidates’ segregated data for the analysis and evaluation to perfectly classify and to show the accurate results by using the proposed Algorithms. The data mining approaches have been used in which HUGO (Highly Undetectable steGO) Algorithm, Naïve Bayes Classification, k-nearest neighbors and Logistic Regression are used with the extension of the other classification methods that are Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) as classifiers. These classifiers are given names for further analysis that are Classifier-1 and Classifier-2 respectively. Along with these, a tool is used named WEKA (Waikato Environment for Knowledge Analysis) for the analysis of the classifier-1 and 2. For performance evaluation and analysis the parameters are used for best classification that which classifier has given best performance and why. These parameters are RRSE (Root Relative Square Error), RAE (Relative Absolute Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). For the best and outstanding accuracy of the proposed work, these parameters have been tested under the simulation environment along with the incorrect, correct classifying and the %age has been witnessed and calculated. From simulation results based on RRSE, RAE, MAE and RMSE, it has been shown that classifier-1 has given outstanding performance among the others and has been placed in highest priority.
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Al-Radaideh, Q. A. Emad, M. A & M. I. A. (2006). Mining scholar data using DTs. The 2006 International Arab Conference on Information Technology; pp. 1–5.
Baradwaj, B. K. S. Pal. (2011). Mining educational data to analyze scholars’ performance. International Journal of Advance Computer Science and Applications; Vol (2); pp. 63–69.
Bunker, K. R., Singh, U. K. & Pandya, B. (2012). Data mining: Estimation for performance improvement of graduate scholars using classification. IEEE; pp. 1–5.
Grivokostopoulou, F., Perikos, I. & Hatzilygeroudis, I. (2014). Utilizing semantic web technologies and data mining techniques to analyze scholars learning and predicts final performance. IEEE. pp. 488–494.
Hoe, A. C. K., Ahmad, M. S., Hooi, T. C., Shanmugam, M., Gunasekaran, S. S., Cob, Z. C. & Ranasamy, A. (2013). Analyzing scholars records to identify patterns of scholars’ performance. International Conference on Research and Innovation in Information Systems (ICRIIS): pp. 544–547.
Khan, A. R., Ahmed, A. & Ahmed, S. (2014, March). Collaborative web based cloud services for E-learning and educational ERP. Proceeding of 2014 RAECS UIET Punjab University Chandigarh, pp. 1–4. IEEE.
Ktona, A ., Xhaja, D. & Ninka, I. (2014). Extracting relationships between scholars’ academic performance and their area of interest using data mining techniques. 2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 6–11.
Mayilvaganan, M. & Kalpanadevi, D. (2014). Comparising of classification techniques for predicting the performance of scholar academic environment. 2014 International Conference on Communication and Network Technologies (ICCNT): pp. 113–118.
Pal, M. (2008). Multiclass approaches for SVM based land cover classification CoRR: pp. 1–16.
Wang, J. Z. Lu. W. Wu & Y. Li. 2012. The application of data mining technology based on teaching information. The 7th International Conference on Computer Science and Education; pp. 652–657.
Bengio, Y., Courville, A., & Vincent, P. (2013). “Representation Learning: A Review and New Perspectives”. IEEE Trans. PAMI, special issue Learning Deep Architectures.
Bengio, Y. & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-scale kernel machines, 34(5), 1-41.
Ciresan, D., & Meier, U. (2015, July). “Multi-column deep neural networks for offline handwritten Chinese character classification”. In 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1-6). IEEE.
Fayyad, U., Piatetsky-shapiro, G. & Smyth, P. (2007). From data mining to knowledge discovery in databases (1996). AI Magazine. Vol 17(3).
Han, J., Pei, J. & Kamber, M. (2011). Data Mining: Concepts and Techniques . Elsevier.
Hu, J., Niu, H., Carrasco, J., Lennox, B. & Arvin, F. (2020). Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology. 69 (12): 14413–14423. doi:10.1109/TVT.2020.3034800.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097–1105.
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an Integration of Deep Learning and Neuroscience. Frontiers in Computational Neuroscience. 10, 94.
Nordbotten, S. (2006). Data mining with Neural Networks. Bergen, Norway.
Ogor, E. N. (2007). Student academic performance monitoring and evaluation using data mining techniques. In Electronics, robotics and automotive mechanics conference (CERMA 2007) (pp. 354–359). IEEE.
Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks. 61: 85–117.
Trevor, H., Robert, T., & Jerome, F. (2009). Hastie T, Friedman J, Tibshirani R. The elements of statistical learning. Vol. 2.
Witten, I. H. & Frank, E. (2002). Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31(1), 76–77.
Hussain, A., Hussain, T. ., & Ullah, I. (2022). The Approach of Data Mining: A Performance-based Perspective of Segregated Data Estimation to Classify Distinction by Applying Diverse Data Mining Classifiers. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 339–359.

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