Comparing Neural Networks with Data Mining techniques to simulate Cu; case study: Parkam Kerman

Document Type : علمی -پژوهشی

Abstract

Data analysis helps us to understand how we should achieve the expected results, so as to achieve more accurate processes, it is necessary to choose an analyzing method that is the best one for our subject. In order to analyze surface samples of Parkam district based on four values of longitude and latitude of sampling points and grades of copper and Molybdenum, we use the three useful method of K-Nearest Neighbor (KNN), K-Means and Neural Networks. One of the important viewpoints in data mining to analyze and investigate high volume of data and samples with different characteristics is clustering viewpoint that itself include different methods and techniques. One of the most famous algorithms of clustering is KNN algorithm to estimate according to the training examples. In fact, it is a non-parametric method used for classification and regression in order to reach relationships among variables while K-Means algorithm tries to divide data in K clusters based on a distance criterion. Neural networks can be useful tools in pattern recognition while there is not much information available for interpretation. In present study, to simulate and estimate copper grade in porphyry copper system of Parkam located in Kerman province, different learning algorithms that are mentioned are compared and results are shown. In this paper, comparing the results of the three algorithms is our target to pave the way of researchers. The results show that KNN has more correlation in contrast of neural networks and K-Means so using KNN can be more effective to estimate copper grade. The advantage of using KNN method relative to other estimation methods in present study is providing a specified and accurate pattern for decision makers in industry to estimate grade.

Keywords

References

  1. منابع
  2. -البرزی، م.، 1389. آشنایی با شبکه‌های عصبی، انتشارات دانشگاه صنعتی شریف، ؟ ص.
  3. -زهرایی، ب. و تکشی، آ.، 1387. کاربرد روش‌های الگوریتم ژنتیک و –K نزدیک‌ترین همسایه در تدوین سیاست‌های بهره‌برداری از مخزن در زمان وقوع سیلاب، نشریه تحقیقات منابع آب، نشریه تحقیقات منابع آب ایران،دوره 3، شماره 12، ص 27-37.
  4. -قنادپور، س.س. و هزارخانی، ا.، 1391. بررسی رفتار سرب نسبت به روی و آهن در کانسار مس پورفیری پرکام، شهر بابک، کرمان با استفاده از روش گروه بندی، نشریه علمی- پژوهش‌های دانش زمین، سال سوم، شماره 9، ص64 - 77.
  5. -قنادپور، س.س. و هزارخانی، ا.، 1392. بررسی چگونگی رفتار عنصر مس نسبت به عناصر مولیبدن، سرب و روی در کانسار مس پورفیری پرکام در استان کرمان، با استفاده از روش K-Means، مجله زمین‌شناسی کاربردی پیشرفته، شماره 7، ص 53 - 63.
  6. -قنادپور، س.س. و هزارخانی، ا.، 1392. برآورد مناطق امید بخش جهت تهیه نقشه‌های ناهنجاری ژئوشیمایی مس و مولیبدن در منطقه پرکام، کرمان. نشریه علمی- پژوهش‌های دانش زمین، سال ششم، شماره 21، ص 40 - 50.
  7. -Andrew, F., Weller, A.J., Harris, J. and Andrew, W., 2007. Two Supervised Neural Networks for Classification of Sedimentary Organic Matter Images from Palynological Preparations: Mathematical Geology, v. 39, p. 657-671.
  8. -Audibert, J.Y. and Tsybakov, A.B., 2007. Fast learning rates for plug-in classifiers under the margin condition: Annals of Statistics, v. 35, p. 608-633.
  9. -Bax, E., 2000. Validation of nearest neighbor classifiers: Ieee transactions information theory, v. 58, p. 2746-2752.
  10. -Bhattacharya, G., Ghosh, K. and Chowdhury, A.S., 2012. An affinity-based new local distance function and similarity measure for kNN algorithm: Pattern Recognition Letters, v. 33, p. 356-363.
  11. -Deegalla, S. and Boström, H., 2007. Classification of Microarrays with kNN: Comparison of Dimensionality Reduction Methods: Intelligent Data Engineering and Automated Learning - IDEAL 2007, v. 4881, p. 800-809.
  12. -Funahashi, K.I., 1989. On the approximate realization of continuous mappings by neural networks: Neural Networks, v. 2, p. 193.
  13. -Ghannadpour, S.S. and Hezarkhani, A., 2015. Investigation of Cu, Mo, Pb and Zn Geochemical behavior and geological interpretations for Parkam Porphyry Copper system, Kerman, Iran: Arabian Journal of Geosciences, v. 8, p. 7273-7284.
  14. -He, J., Tan, A. and Tan, C., 2000. Comparative Study on Chinese Text Categorization Methods, On the PRICAI 2000 Workshop on Text and Web Mining, Melbourne, p. 25-31.
  15. -Kamel, N., Ouchen, I. and Baali, K., 2014. A Sampling-PSO-K-means Algorithm for Document Clustering, Genetic and Evolutionary Computing: Springer International Publishing Switzerland, 238 p.
  16. -Kuo, R.J., Suryani, E. and Yasid, A., 2013. Automatic Clustering Combining Differential Evolution Algorithm and k -Means Algorithm: Proceedings of the Institute of Industrial Engineers Asian Conference 2013, Springer Singapore.
  17. -Lacassie, J.P., Roser, B.P., Ruiz-del-Solar, J., Roser, B. and Herv´e, F., 2006. Visualization of Volcanic Rock Geochemical Data and Classification with Artificial Neural Networks: Math Genealogy, v. 38, p. 697-710.
  18. -Meshkani, S.A., Mehrabi, B., Yaghubpur, A. and Alghalandis, Y.F., 2011. The application of geochemical pattern recognition to regional prospecting: A case study of the Sanandaj-Sirjan metallogenic zone, Iran: Journal of Geochemical Exploration, v. 108, p. 183-195.
  19. -Mora, J.L., Armas-Herrera, C.M., Guerra, J.A., Rodriguez, A. and Arbelo, C.D., 2012. Factors affecting vegetation and soil recovery in the Mediterranean woodland of the Canary Islands (Spain): Journal of Arid Environments, v. 87, p. 58-66.
  20. -Ozturk, M.M. and Cavusoglu, U. and Zengin, A., 2015. A novel defect prediction method for web pages using k-means++: journal of Export Systems with Applications, v. 42, p. 6496-6506.
  21. -Poloczek, J., Treiber, N.A. and Kramer, O., 2014. KNN Regression as Geo-Imputation Method for Spatio-Temporal Wind Data: International Joint Conference SOCO’14-CISIS’14-ICEUTE’14, v. 299, p. 185-193.
  22. -Shang, W., Huang, H., Zhu, H., Lin, Y., Qu, Y. and Dong, H., 2006. An Adaptive Fuzzy kNN Text Classifier: Computational Science – ICCS 2006, p. 216-223.
  23. -Singer, D.A., 2006. Typing mineral deposits using their associated rocks and grades and tonnages in a probabilistic neural network: Mathematics Genealogy, v. 38, p. 465-475.
  24. -Tarkian, M. and Stribrny, B., 1999. Platinum-group elements in porphyry copper deposits: a reconnaissance study: Mineralogy and Petrology, Springer-Verlag, v. 65, p. 161-183.
  25. -Varaprasad, M., 2012. Algorithm for Clustering with Intrusion Detection Using Modified and Hashed K – Means Algorithms: Engineering & Applications, Springer-New Delhi, India, v. 167, p. 737-744.
  26. -Weller, A.F., Corcoran, J., Harris, A.J. and Ware, J.A., 2005. The semi-automated classification of sedimentary organic matter in palynological preparations: Computer and Geoscience, v. 31, p. 1213-1223.
  27. -Wu, X. and Zhou, Y., 1993. Reserve estimation using neural network techniques: Computer and Geoscience, v. 19, p. 567-575.
  28. -Xu, H., Lu, Sh. and Zhou, Sh., 2013. A Novel Algorithm for Text Classification Based on KNN and Chaotic Binary Particle Swarm Optimization: Proceedings of the 2012 International Conference on Information Technology and Software Engineering, v. 211, p. 619-627.
  29. -Yang, Y., 1999. An evaluation of statistical approaches to text categorization: Journal of Information Retrieval, v. 1, p. 69-90.
  30. -Yang, Y. and Liu, X., 1999. A re-examination of text categorization methods: In proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99), p. 42-49.
  31. -Yang, J., Zhuang, Y. and Wu, F., 2012. ESVC- based extraction and segmentation of texture features: Computers and Geosciences, v. 49, p. 238-247.
  32. -Yi, X. and Zhang, Y., 2013, Equally contributory privacy-preserving K-Means clustering over vertically partitioned data: Elsevier, Information Systems, v. 38, p. 97-107.
  33. -Zhang, C., 2013. Study on the Application of Fuzzy KNN to Chinese and English Recognition: Proceedings of the 2012 International Conference of Modern Computer Science and Applications, v. 191, p. 327-332.
  34. -Zu-Feng, W., Xiao-Fan, M., Qiao, L. and Zhi-guang, Q., 2014. Logical Symmetry Based K-means Algorithm with Self-adaptive Distance Metric: Advances in Computer Science and its Applications, Springer-Verlag, Berlin Heidelberg. v. 279, p. 929-936.

Files

Share

How to cite

Statistics