TY  - JOUR
T1  - A New Fuzzy Clustering by Outliers
AU - Dik, Amina AU - Jebari, Khalid AU - Bouroumi, Abdelaziz AU - Ettouhami, Aziz 
JO  - Journal of Engineering and Applied Sciences
VL  - 9
IS  - 10
SP  - 372
EP  - 377
PY  - 2014
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2014.372.377
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2014.372.377
KW  - Similarity measure
KW  -outlier detection
KW  -FCM
KW  -proximity degree
KW  -illustrated
AB  - 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.
ER  -