An Improved Robust Fuzzy Algorithm for Unsupervised Learning

Journal of Intelligent Systems

Dik Amina¹  Jebari Khalid²  Ettouhami Aziz² 

[1] Conception and Systems Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Rabat 10001,Morocco;Conception and Systems Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Rabat,Morocco;

关键词: similarity measure;    outlier detection;    clustering;    proximity degree;   

DOI  :  10.1515/jisys-2018-0030

来源: DOAJ

【 摘 要 】

This paper presents a robust, dynamic, and unsupervised fuzzy learning algorithm (RDUFL) that aims to cluster a set of data samples with the ability to detect outliers and assign the numbers of clusters automatically. It consists of three main stages. The first (1) stage is a pre-processing method in which possible outliers are determined and quarantined using a concept of proximity degree. The second (2) stage is a learning method, which consists in auto-detecting the number of classes with their prototypes for a dynamic threshold. This threshold is automatically determined based on the similarity among the detected prototypes that are updated at the exploration of a new data. The last (3) stage treats quarantined samples detected from the first stage to determine whether they belong to some class defined in the second phase. The effectiveness of this method is assessed on eight real medical benchmark datasets in comparison to known unsupervised learning methods, namely, the fuzzy c-means (FCM), possibilistic c-means (PCM), and noise clustering (NC). The obtained accuracy of our scheme is very promising for unsupervised learning problems.

【 授权许可】

Unknown