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A New Modified Technique to Identify Outlier Values Using Fuzzy Clustering | ||
| Journal of University of Anbar for Pure Science | ||
| Volume 19, Issue 1 - Serial Number 19, May and June 2025, Pages 260-268 PDF (1.16 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.37652/juaps.2024.150715.1273 | ||
| Authors | ||
| Wafaa S. Hasanain* ; Saja Mohammed Sakran | ||
| Department of Mathematics, College of Science, Mustansiriyah University, Baghdad, Iraq | ||
| Abstract | ||
| Outliers within a dataset are data points that substantially differ from the rest of the data. These atypical data points can be attributed to a range of factors, such as errors in measurement, issues with data input, and natural variations in the data. Managing outliers is essential to ensure the integrity of statistical analyses and to avoid obtaining misleading results. These outliers can be observed at either the very high or very low points, possess the capacity to exert a notable effect on statistical measures such as the mean and variance. Many diagnostic techniques focus on affect centroids and distances between clusters to detect these abnormal points. In this particular study, fuzzy cluster techniques are primarily employed to identify outliers within the dataset. we proposed an alternative technique to detect outliers by using the concept and approach of standardization by using fuzzy cluster techniques in detection outliers in data set. The performance of the proposed method is compared with the others by using simulation. | ||
| Keywords | ||
| Fuzzy c-means; Possibilistic c-means; Standarization; Objective function; Simulation | ||
| References | ||
|
[1] zadeh, L. A. (1965). fuzzy set, information and control. 338-353.
[2] zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning, Part 1. Information Science, 199-249.
[3] Rhee, F. C., & Hwang, C. (2001). type-2 fuzzy c-means clustering algorithm. in EEE 9th IFSA World Congress and 20th NAFIPS Conf.
[4] Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms, Plenum.
[5] Dunn, J. (1974). A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3, 32-57.
[6] Gustafson, D. E., & Kessel, W. C. (1979). Fuzzy clustering with a fuzzy covariance matrix.. in In Proc. of IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, San Diego.
[7] Ōhashi, Y. (1984). Fuzzy Clustering and Robust Estimation. Paper in University Tokyo Hospital.
[8] Dave, R. N. (1991). Characterization and Detection of Noise in Clustering. Pattern Recognition Letters, 12, 657-664.
[9] Dave, R., & Krishnapuram N. (1997). Robust Clustering Methods: A Unified View. IEEE Trans. Fuzzy Systems, 5(2), 270-293.
[10] Bandemer, H., & Gottwald, S. (1993). Einfiihrung in Fuzzy-Methoden. (4. Aufl.), Berlin: Akademie Verlag.
[11] Krishnapuram, R., & Keller J. (1993). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1(2), 98-110.
[12] Krishnapuram, R., & Keller J. (1996). The possibilistic c-means algorithm: insights and recommendations. IEEE Transactions on Fuzzy Systems, 4(3), 385-393.
[13] Pal, N. R., Pal, K., & Bezdek, J. C. (1997). A mixed c-means clustering model in In Proc. of the 6th IEEE Int. Conf. on Fuzzy Systems (Vol. 3).
[14] Pal, N. R., Pal, K., Keller, J. M., & Bezdek, J. C. (2005). A Possibilistic Fuzzy C-Means Clustering Algorithm," IEEE Transactions on Fuzzy Systems, 13(4), 517-530.
[15] Yang, M. S., & Wu, K. L. (2006). Unsupervised possibilistic clustering. Pattern Recognition, 39(1), 5-21.
[16] Wu, X., Wu, B., Sun, J., & Fu, H. (2010). Unsupervised possibilistic fuzzy clustering. J. of Information and Computational Sci.. 7(5), 1075-1080.
[17] Zgurovsky, M. Z., & Zaychenko, Y. P. (2020). Big Data: Conceptual Analysis and Applications, Springer Nature Switzerland AG.
[18] Höppner, F., Klawonn, F., & Kruse R. (2000). Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, Wiley IBM PC Series, 1st Edition.
[19] Bezdek, J. C. (1974). Cluster validity with fuzzy sets. J. Cybernetics, 3, 58-73.
[20] Filippone, M., Masulli, F., & Rovetta, S. (2007). Possibilistic clustering in feature space. In Int. Workshop on Fuzzy Logic and Applications. Springer, Berlin, Heidelberg, 219-226.
[21] Timm, H., Borgelt, C., Döring, C., & Kruse R. (2004). An extension to possibilistic fuzzy cluster analysis. Fuzzy Sets and Systems, 147(1), 3-16.
[22] Duda, R., & Hart, P. (1973). Pattern Classification and Scene Analysis, NEWYORK: WILEY. | ||
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