Data Quality Enhancement Technology to Improve Decision Support - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 77

4. Conclusions
Outlier and noise are part of uncertainty that arises due to mechanical faults, changes in
system behavior, fraudulent behavior, network intrusions, human errors, keyboard error,
hand writing error and so on that affect on measurement of Gaussian membership function
parameters. In Gaussian there are two parameters, Mean and Standard deviation that are
tuned based on dataset, therefore if we do not extract useful knowledge or desired clustered
data from dataset, Mean and Standard deviation will not be accurate parameters for
Gaussian membership function. From the huge number of clustering methods, Fuzzy C-
Mean clustering is flexible, moveable, creatable, elimination of classes and any their
combination. Since the degree of membership function on an object to the classes found
provides a strong tool for the identification of changing class structures. Fuzzy C-Mean in
order to build an initial classifier and to update our classifier in each cycle, thus we utilized
Fuzzy c-mean clustering with statistic equation to remove noisy data and detect outlier and
mine valuable data to get accurate result with Type-1 Fuzzy Logic Systems and gradient
descent algorithm.
By applying proposed method, the quality of data has been improved (As shown in Table
3). The proposed method enhanced the data quality. Thus, by improving the quality of data,
the accurate decision making will be achieved in decision support system.
5. References
[1] Russell, G., Grossett:“Webster's New Dictionary and Thesaurus”. Geddes and Grosset Ltd.,
New Lanark, Scotland, 1990.
[2] Rahman, A., Handling imprecision and uncertainty in software quality models. 2005.
[3] Mendel, J.M., Uncertain rule-based fuzzy logic systems: introduction and new directions . 2000:
Prentice Hall.
[4] Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning.
Information sciences, 1975. 8(3): p. 199-249.
[5] Last, M. and A. Kandel. Automated detection of outliers in real-world data . 2001: Citeseer.
[6] Cherednichenko, S., Outlier detection in clustering. 2005.
[7] Wand, Y. and R.Y. Wang, Anchoring data quality dimensions in ontological foundations. 1996.
[8] Wang, R.Y., M.P. Reddy, and H.B. Kon, Toward quality data: An attribute-based approach.
Decision Support Systems, 1995. 13(3-4): p. 349-372.
[9] Clarke, E. and T. Coladarci, i> Elements of Statistical Reasoning.</i> New York . 1999, Wiley.
[10] Mitchel, T., Machine learning. Machine Learning, 1997. 48(1).
[11] Pyle, D., Data preparation for data mining . 1999: Morgan Kaufmann Pub.
[12] Rehm, F., F. Klawonn, and R. Kruse, A novel approach to noise clustering for outlier
detection. Soft Computing-A Fusion of Foundations, Methodologies and
Applications, 2007. 11(5): p. 489-494.
[13] Kirkby, R. and E. Frank, WEKA Explorer User Guide for Version 3-5-3. University of
Waikato,(June 2006), 2006.
[14] Crespo, F. and R. Weber, A methodology for dynamic data mining based on fuzzy clustering.
Fuzzy Sets and Systems, 2005. 150(2): p. 267-284.
[15] Macfie, B.P. and P.M. Nufrio, Applied statistics for public policy . 2006: ME Sharpe Inc.
[16] Dunn, J.C., A fuzzy relative of the ISODATA process and its use in detecting compact well-
separated clusters. Cybernetics and Systems, 1973. 3(3): p. 32-57.