A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling

2012 ◽  
pp. 499-508
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy System ◽  
Distance Measure ◽  
System Model ◽  
Rule Base ◽  
Optimization Approach ◽  
Fuzzy Data ◽  
System Models ◽  
Fuzzy System Models

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valuate Type-2 fuzzy clustering approach. In this index, all operations are fuzzy and the minimization method is fuzzy ranking with Hamming distance. The proposed Type-2 fuzzy clustering method is used for development of indirect approach to Type-2 fuzzy modeling, where the rules are extracted from clustering fuzzy numbers (Zadeh, 1965). Then, the Type-2 fuzzy system is tuned by an inference algorithm for optimization of the main parameters of Type-2 parametric system. In this case, the parameters are: Schweizer and Sklar t-Norm and s-Norm, a-cut of rule-bases, combination of FATI and FITA inference approaches, and Yager parametric defuzzification. Finally, the proposed Type-2 fuzzy system model is applied in prediction of the steel additives in steelmaking process. It is shown that, the proposed Type-2 fuzzy system model is superior in comparison with multiple regressions and Type-1 fuzzy system model, in terms of the minimization the effect of uncertainty in the rule-base fuzzy system models an error reduction.

scholarly journals Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3210
Author(s):  
Sana Qaiyum ◽  
Izzatdin Aziz ◽  
Mohd Hilmi Hasan ◽  
Asif Irshad Khan ◽  
Abdulmohsen Almalawi
Keyword(s):  
Fuzzy Clustering ◽  
Data Streams ◽  
Clustering Algorithms ◽  
Real Life ◽  
Fuzzy Cluster ◽  
Optimization Approach ◽  
Cluster Validity Index ◽  
Data Points ◽  
Interval Type

Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets.

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