@article{MAKHILLJEAS201491013474,
    title = {A New Fuzzy Clustering by Outliers},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {9},
    number = {10},
    pages = {372-377},
    year = {2014},
    issn = {1816-949x},
    doi = {jeasci.2014.372.377},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2014.372.377},
    author = {Amina,Khalid,Abdelaziz and},
    keywords = {Similarity measure,outlier detection,FCM,proximity degree,illustrated},
    abstract = {This study presents a new approach for partitioning data sets affected by outliers. The proposed scheme consists of two main stages. The first stage is a preprocessing technique that aims to detect data value to be outliers by introducing the notion of object’s proximity degree. The second stage is a new procedure based on the Fuzzy C-Means (FCM) algorithm and the concept of outliers clusters. It consists to introduce clusters for outliers in addition to regular clusters. The proposed algorithm initializes their centers by the detected possible outliers. Final and accurate decision is made about these possible outliers during the process. The performance of this approach is also illustrated through real and artificial examples.}
    }