A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise

  • Authors

    • Kwang Kyu Lee
    • . .
    https://doi.org/10.14419/ijet.v7i3.33.18592

    Received date: August 29, 2018

    Accepted date: August 29, 2018

    Published date: August 29, 2018

  • Data Clustering Algorithm, Data Mining, DBSCAN, FCM, Fuzzy C-Means, K-means

  • Abstract

    Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzzy C-Means(FCM)[11,12,13] is a very important clustering technique based on fuzzy logic. DBSCAN(Density Based Spatial Clustering of Applications with Noise)[8] is a density-based clustering algorithm that is suitable for dealing with spatial data including noise and is a collection of arbitrary shapes and sizes. In this paper, we compare and analyze the performance of Fuzzy C-Means and DBSCAN algorithms in different data sets.

  • References

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  • How to Cite

    Kyu Lee, K., & ., . (2018). A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise. International Journal of Engineering and Technology, 7(3.33), 131-133. https://doi.org/10.14419/ijet.v7i3.33.18592