An Improved Robust Fuzzy Algorithm for Unsupervised Learning

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 i...

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Bibliographic Details
Published in:Journal of intelligent systems 2020-01, Vol.29 (1), p.1028-1042
Main Authors: Dik, Amina, Jebari, Khalid, Ettouhami, Aziz
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Data analysis
Fuzzy sets
Machine learning
outlier detection
Outliers (statistics)
Prototypes
proximity degree
Quarantine
Robustness (mathematics)
Similarity measure
Teaching methods
Unsupervised learning
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Summary: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.
ISSN:0334-1860
2191-026X
DOI:10.1515/jisys-2018-0030