Data mining and knowledge discovery in databases have been considered as a significant research area in industry. This survey presents an overview, description and future directions which depict a standard for knowledge discovery and data mining process model. The paper mentions particular real-world applications, specific data mining techniques, challenges involved in real-world application of knowledge discovery, current and future research ideas in the field. The applications to both academic and industrial problems are discussed. The main target of the review is the consolidation of the research in this particular area and thereby helping in enhancing the existing model by embedding other current standards.

Knowledge discovery database, data mining, real world application

[1] Kurgan, L. A., &Musilek, P. (2006). A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review, 21(1), 1-24.
[2] Han, J., & Fu, Y. (1994, July). Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases. In KDD Workshop (pp. 157-168).
[3] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.
[4] Jing, Y., Li, T., Fujita, H., Wang, B., & Cheng, N. (2018). An incremental attribute reduction method for dynamic data mining. Information Sciences, 465, 202-218.
[5] Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., &Verkamo, A. I. (1996). Fast discovery of association rules. Advances in knowledge discovery and data mining, 12(1), 307-328.
[6] Apte, C., & Hong, S. J. (1994, July). Predicting Equity Returns from Securities Data with Minimal Rule Generation. In KDD Workshop (pp. 407-418).
[7] Berry, L. L. (1995). Relationship marketing of services—growing interest, emerging perspectives. Journal of the Academy of marketing science, 23(4), 236-245.
[8] Brachman, R. J., & Anand, T. (1996, February). The process of knowledge discovery in databases. In Advances in knowledge discovery and data mining (pp. 37-57). American Association for Artificial Intelligence.
[9] Jain, A. K., &Dubes, R. C. (1988). Algorithms for clustering data.
[10] Brodley, C. E., & Smyth, P. (1997). Applying classification algorithms in practice. Statistics and Computing, 7(1), 45-56.
[11] Senator, T. E., Goldberg, H. G., Wooton, J., Cottini, M. A., Khan, A. U., Klinger, C. D. & Wong, R. W. (1995). Financial Crimes Enforcement Network AI System (FAIS) Identifying Potential Money Laundering from Reports of Large Cash Transactions. AI magazine, 16(4), 21.
[12] Sak, H., Senior, A., &Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Fifteenth annual conference of the international speech communication association.
[13] Priyadharsini, C., &Thanamani, A. S. (2014). An Overview of Knowledge Discovery Database and Data mining Techniques. International Journal of Innovative Research in Computer and Communication Engineering, 2(1).
[14] Burbidge, R., & Buxton, B. (2001). An introduction to support vector machines for data mining. Keynote papers, young OR12, 3-15.
[15] Du, J., Zhou, J., Li, C., & Yang, L. (2016, August). An overview of dynamic data mining. In Informative and Cybernetics for Computational Social Systems (ICCSS), 2016 3rd International Conference on (pp. 331-335). IEEE.
[16] Suresh, K., Jnaneswari, C., Kranthi, G. L., & Bindu, K. Knowledge Discovery in Datamining Using Soft Computing. vol, 3, 3952-3957.
[17] Al-mamory, S. O., &Jassim, F. S. (2013). Evaluation of different data mining algorithms with KDD CUP 99 Data Set. Journal of University of Babylon, 21(8), 2663-2681.
[18] Bhavsar, H., & Panchal, M. H. (2012). A review on support vector machine for data classification. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(10), pp-185.
[19] Yeung, D. Y., & Chow, C. (2002). Parzen-window network intrusion detectors. In Object recognition supported by user interaction for service robots (Vol. 4, pp. 385-388). IEEE.
[20] Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. A Wiley-Interscience Publication, New York: Wiley, 1973.
[21] Panda, M., & Patra, M. R. (2009). Evaluating machine learning algorithms for detecting network intrusions. International journal of recent trends in engineering, 1(1), 472.
[22] Ramadas, M., Ostermann, S., &Tjaden, B. (2003, September). Detecting anomalous network traffic with self-organizing maps. In International Workshop on Recent Advances in Intrusion Detection (pp. 36-54). Springer, Berlin, Heidelberg.
[23] Nguyen, H. A., & Choi, D. (2008, October). Application of data mining to network intrusion detection: classifier selection model. In Asia-Pacific Network Operations and Management Symposium (pp. 399-408). Springer, Berlin, Heidelberg.
[24] Bharti, K. K., Shukla, S., & Jain, S. (2010). Intrusion detection using clustering. Proceeding of the Association of Counseling Center Training Agencies (ACCTA), 1.
[25] Hemanth, K. S., Vastrad, C. M., &Nagaraju, S. (2011, January). Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets. In International Conference on Computer Science and Information Technology (pp. 512-522). Springer, Berlin, Heidelberg.
[26] Adhikari, A., Jain, L. C., & Prasad, B. (2017). A State-of-the-Art Review of Knowledge Discovery in Multiple Databases. Journal of Intelligent Systems, 26(1), 23-34.
[27] Han J, Kamber M. “Data Mining: Concepts and Techniques”. 2/e San Francisco: CA. Morgan Kaufmann Publishers, an imprint of Elsevier.2006. pp-5-38.
[28] Hiremath, R., & Patil, P. (2016). Astudy-Knowledge Discovery Approachesand Its Impact with Reference to Cognitive Internet of Things (Ciot). International Journal of Information, 6(1/2).
[29] Anand, S. S., Patrick, A. R., Hughes, J. G., & Bell, D. A. (1998). A data mining methodology for cross-sales. Knowledge-based systems, 10(7), 449-461.
[30] Butler, S. (2002). An investigation into the relative abilities of three alternative data mining methods to derive information of business value from retail store-based transaction data (Doctoral dissertation, BSc thesis, School of Computing and Mathematics, Deakin University, Australia).
[31] Cios, K. J., & Moore, G. W. (2001). Medical data mining and knowledge discovery: Overview of key issues. Studies in Fuzziness and Soft Computing, 60, 1-20.
[32] Moyle, S., Bohanec, M., &Osrowski, E. (2003). Large and tall buildings: a case study in the application of decision support and data mining. KLUWER INTERNATIONAL SERIES IN ENGINEERING AND COMPUTER SCIENCE, 191-202.
[33] Sacha, J. P., Cios, K. J., &Goodenday, L. S. (2000). Issues in automating cardiac SPECT diagnosis. IEEE Engineering in Medicine and Biology Magazine, 19(4), 78-88.
[34] Hand, D. J. (1994). Deconstructing statistical questions. Journal of the Royal Statistical Society. Series A (Statistics in Society), 317-356.
[35] Hall, J., Mani, G., & Barr, D. (1996). Applying computational intelligence to the investment process. Proceedings of CIFER-96: Computational Intelligence in Financial Engineering. Washington, DC: IEEE Computer Society.
[36] Suresh, K., Jnaneswari, C., Kranthi, G. L., & Bindu, K. Knowledge Discovery in Datamining Using Soft Computing. vol, 3, 3952-3957.
[37] Kusum K. Bharti, S. Shukla and S. Jain, “Intrusion detection using clustering”, vol.1, issue 2, 3, 4, pp.6, 2010
[38] Manikantan R., S. Ostermann, and B. Tjaden,” Detecting Anomalous Network Traffic with Self-organizing Maps”, Ohio University, pp.37, 2003.
[39] Huy A. N., D. Choi,” Application of Data Mining to Network Intrusion Detection: Classifier Selection Model”, pp:1, 2008.
[40] Mrutyunjaya P. and M. Ranjan Patra,” Evaluating Machine Learning Algorithms for Detecting Network Intrusions”, International Journal of Recent Trends in Engineering, vol. 1, no.1, May 2009.
[41] Richard O. Duda and P. E. Hart, “Pattern Classification and Scene Analysis”, New York: Wiley, pp: 78, 1973.
[42] Qiang W., V. Megalooikonomou, “A Clustering Algorithm for Intrusion Detection”, pp:3, 2004