مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Information Journal Paper

Title

OUTLIER DETECTION BY WEIGHTED MERCER-KERNEL BASED FUZZY CLUSTERING ALGORITHM

Pages

  129-136

Abstract

 Outliers are data values that lie away from the general cluster of other data values. Detecting the OUTLIERS of a dataset is an important research topic for data cleaning and finding new useful knowledge in many research areas, i.e. data mining, pattern recognition, etc. In the past decades, many useful algorithms were proposed in the literature. In this paper, a new fuzzy kernel-clustering algorithm with OUTLIERS (FKCO) is presented to locate critical areas that are often represented by only a few OUTLIERS. Theoretic analysis also shows that FKCO can converge to a local minimum of the objective function. Finally, based on the information theory, a new criterion for finding OUTLIERS is also proposed. Simulations of different types of datasets demonstrate the feasibility of this new method.

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    Cite

    APA: Copy

    SHEN, H., YANG, J., DONG, Y., & WANG, S.. (2005). OUTLIER DETECTION BY WEIGHTED MERCER-KERNEL BASED FUZZY CLUSTERING ALGORITHM. IRANIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (IJECE), 4(2), 129-136. SID. https://sid.ir/paper/283381/en

    Vancouver: Copy

    SHEN H., YANG J., DONG Y., WANG S.. OUTLIER DETECTION BY WEIGHTED MERCER-KERNEL BASED FUZZY CLUSTERING ALGORITHM. IRANIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (IJECE)[Internet]. 2005;4(2):129-136. Available from: https://sid.ir/paper/283381/en

    IEEE: Copy

    H. SHEN, J. YANG, Y. DONG, and S. WANG, “OUTLIER DETECTION BY WEIGHTED MERCER-KERNEL BASED FUZZY CLUSTERING ALGORITHM,” IRANIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (IJECE), vol. 4, no. 2, pp. 129–136, 2005, [Online]. Available: https://sid.ir/paper/283381/en

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