Functions, Processes, Stages And Application Of Data Mining

Data mining is an automatic information discovery process by identifying patterns from large data sets or databases. The process of finding information can be done by a method of grouping data into several groups from a data set that is in data mining called the clustering method. Data mining is defined as a method of finding information that is hidden in a database that is large and difficult to obtain by using only ordinary queries. Unit Implementation and Testing, software design is a series of programs or program units. Then unit testing involves verifying that each program unit meets its specifications (Sommerville, 2003). Programs should be released after they are developed, tested to correct errors found in their quality guarantee testing (Padmini, 2005). There are two testing methods, namely: (1) The white box method is a test that focuses on the internal logic of the software (program source code); (2) The black box method is to provide actual results in accordance with the results needed. In the testing phase, the author conducted a black box method that tests the functionality of the software alone without having to know the internal structure of the program (source code).

REFERENCES

[1] Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993.
[2] MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967.
[3] Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.
[4] Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94.
[5] McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York.
[6] Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In WWW-7, 1998.
[7] Freund, Y. and Schapire, R. E. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.
[8] Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6).
[9] Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398.
[10] L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984.
[11] Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In SIGMOD '00.
[12] Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. SODA, 1998.
[13] Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96.
[14] Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, 1996.
[15] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01.
[16] Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98.
[17] Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992.
[18] Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern Mining. In ICDM '02.
[19] Turban, E, 2005, Decision Support Systems and Intelligent Systems Indonesian Edition Volume 1. Andi: Yogyakarta.
[20] Larose, Daniel T. 2005. Discovering Knowledge in Data: An Introduction to Data Mining. John Willey & Sons, Inc.
[21] ayyad, Usama. 1996. Advances in Knowledge Discovery and Data Mining. MIT Press.
[22] https://beyonder.asia/pengertian-fungsi-proses-dan-tahapan-data-mining/