An Extended Membrane System with Active Membranes to Solve Automatic Fuzzy Clustering Problems

2016 ◽  
Vol 26 (03) ◽  
pp. 1650004 ◽  
Author(s):  
Hong Peng ◽  
Jun Wang ◽  
Peng Shi ◽  
Mario J. Pérez-Jiménez ◽  
Agustín Riscos-Núñez
Keyword(s):  
Fuzzy Clustering ◽  
Membrane Structure ◽  
Membrane System ◽  
Benchmark Problems ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Automatic Clustering ◽  
Active Membranes ◽  
Comparison Results ◽  
Fuzzy Clustering Method

This paper focuses on automatic fuzzy clustering problem and proposes a novel automatic fuzzy clustering method that employs an extended membrane system with active membranes that has been designed as its computing framework. The extended membrane system has a dynamic membrane structure; since membranes can evolve, it is particularly suitable for processing the automatic fuzzy clustering problem. A modification of a differential evolution (DE) mechanism was developed as evolution rules for objects according to membrane structure and object communication mechanisms. Under the control of both the object’s evolution-communication mechanism and the membrane evolution mechanism, the extended membrane system can effectively determine the most appropriate number of clusters as well as the corresponding optimal cluster centers. The proposed method was evaluated over 13 benchmark problems and was compared with four state-of-the-art automatic clustering methods, two recently developed clustering methods and six classification techniques. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness and robustness.

Parameter Prediction Based on Type-2 Fuzzy Clustering

2018 ◽  
Vol 26 (06) ◽  
pp. 877-892 ◽  
Author(s):  
Türkan Erbay Dalkiliç ◽  
Seda Sağirkaya
Keyword(s):  
Fuzzy Logic ◽  
Regression Analysis ◽  
Fuzzy Clustering ◽  
Alternative Methods ◽  
Data Set ◽  
Clustering Problem ◽  
Fuzzy Clustering Method ◽  
Parameter Prediction

In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is discussed. Firstly, type-1 fuzzy clustering problem was solved by the fuzzy c-means (FCM) method when the fuzzifier index is equal to two. Then the fuzzifier index m is defined as interval number. The membership degrees to the sets are determined by type-2 fuzzy clustering method. Membership degree obtained as a result of clustering based on type-1 and type-2 fuzzy logic are used as weight and parameter prediction using these membership degrees that determined by the proposed algorithm. Finally, the prediction result of the type-1 and type-2 fuzzy clustering parameter is compared with the error criterion based on the difference between observed values and the predicted values.

Extension-Based Clustering Method: An Approach to Support Adaptable Design of the Product

2007 ◽  
Author(s):  
Yanwei Zhao ◽  
Huijun Tang ◽  
Nan Su ◽  
Wanliang Wang
Keyword(s):  
Fuzzy Clustering ◽  
Design Method ◽  
Direct Method ◽  
Threshold Value ◽  
Minimal Distance ◽  
Clustering Methods ◽  
Clustering Method ◽  
Adaptable Design ◽  
Fuzzy Clustering Method ◽  
Distance Formula

Design for product adaptability is one of the techniques used to provide customers with products that exactly meet their requirements. Clustering methods have been used extensively in the study of product adaptability design. Of the clustering methods, the fuzzy clustering method is the most widely in the design field. The three main kinds of fuzzy clustering methods are the transitive closure method, the dynamic direct method and the maximum tree method. The dynamic direct clustering method has been found to produce design solutions with the lowest cost. In this paper, a new approach for obtaining adaptable product designs using the clustering method is proposed. The method consists of three steps. Firstly, the extension distance formula is used to determine the distance between two products in a product database. The product design space and the distances between individuals are used as grouping criteria in this step. Secondly, the minimal distance between products is used to obtain the clustering index. Thirdly, the threshold value is used to divide the products in the database into groups. Customer demands and the results obtained from the adaptable function (based on the extension distance formula) are used to evaluate the fitness of the groups and their corresponding products. The product with the largest adaptable function value to demand ratio is selected product. In order to the show the advantage of using the extension-clustering method, both the extension-clustering method and the dynamic direct method are presented and compared. The comparison indicates that the extension-clustering method leads to quicker evaluations of design alternatives and results that more closely match customers’ demands. An example of the adaptable design of circular saws tools is used to demonstrate that with the extension-clustering design method a high variety of intelligent configurations can be obtained with significant rapidity.

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

scholarly journals ONLINE PROBABILISTIC FUZZY CLUSTERING METHOD BASED ON EVOLUTIONARY OPTIMIZATION OF CAT SWARM

2021 ◽  
pp. 65-70
Author(s):  
Ye. V. Bodyanskiy ◽  
A. Yu. Shafronenko ◽  
I. N. Klymova
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Evolutionary Optimization ◽  
Clustering Methods ◽  
Classification Problems ◽  
Probabilistic Data ◽  
Fuzzy Clustering Method ◽  
Clustering And Classification

Context. The problems of big data clustering today is a very relevant area of artificial intelligence. This task is often found in many applications related to data mining, deep learning, etc. To solve these problems, traditional approaches and methods require that the entire data sample be submitted in batch form. Objective. The aim of the work is to propose a method of fuzzy probabilistic data clustering using evolutionary optimization of cat swarm, that would be devoid of the drawbacks of traditional data clustering approaches. Method. The procedure of fuzzy probabilistic data clustering using evolutionary algorithms, for faster determination of sample extrema, cluster centroids and adaptive functions, allowing not to spend machine resources for storing intermediate calculations and do not require additional time to solve the problem of data clustering, regardless of the dimension and the method of presentation for processing. Results. The proposed data clustering algorithm based on evolutionary optimization is simple in numerical implementation, is devoid of the drawbacks inherent in traditional fuzzy clustering methods and can work with a large size of input information processed online in real time. Conclusions. The results of the experiment allow to recommend the developed method for solving the problems of automatic clustering and classification of big data, as quickly as possible to find the extrema of the sample, regardless of the method of submitting the data for processing. The proposed method of online probabilistic fuzzy data clustering based on evolutionary optimization of cat swarm is intended for use in hybrid computational intelligence systems, neuro-fuzzy systems, in training artificial neural networks, in clustering and classification problems.

scholarly journals Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO)

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jian Zhang ◽  
Zongheng Ma
Keyword(s):  
Fuzzy Clustering ◽  
Hybrid Method ◽  
Hybrid Methods ◽  
Clustering Methods ◽  
Cluster Validity ◽  
Partitional Clustering ◽  
Fuzzy Clustering Method ◽  
Clustering Effect ◽  
Partition Clustering ◽  
Improved Accuracy

Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect.

scholarly journals Classification of Countries According to their Tourism Statistics via Different Cluster Analysis Methods and the Place of Turkey in this Structure

2013 ◽  
Author(s):  
Selay Giray
Keyword(s):  
Cluster Analysis ◽  
Hong Kong ◽  
Russian Federation ◽  
Fuzzy Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Analysis Methods ◽  
Fuzzy Clustering Method ◽  
The World

The aim of this study is to classify the countries according to their tourism indicators via different cluster analysis methods and compare the findings. Using classical cluster analysis and fuzzy clustering together will be more appropriate to determine the World tourism structure. In this way the findings can be interpreted more detailed and comparatively. Data obtained from website of Worldbank (3 basic international tourism statistics of 159 countries for the year 2010) and findings are gained using NCSS (statistical software) 2007. According to the findings of fuzzy clustering method, Turkey belogs to a cluster which contains ABD, United Kingdom, China, Austria, France, Germany, Italy, Malaysia, Spain, Hong Kong, Russian Federation, and Ukraine. According to the findings of classical clustering method (k means), Turkey is in the same cluster with same countries except Hong Kong. Also the findings of two techniques are similar about Turkey. Such a result can be expected correspondingly grading the countries about international their tourism data in 2011. Different clustering methods findings are steady about Euroasian countries too. Except Russian Federation and Ukraine all of the other Euroasian countries are located together in same cluster depending upon two different clustering methods. In conclusion two different clustering methods provide consistent (similar) results about the classification of countries according their internatianol tourism statistics.

scholarly journals A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242197
Author(s):  
Paolo Giordani ◽  
Serena Perna ◽  
Annamaria Bianchi ◽  
Antonio Pizzulli ◽  
Salvatore Tripodi ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Mobile Health ◽  
Functional Data ◽  
Ad Hoc ◽  
Health Data ◽  
Allergic Rhinoconjunctivitis ◽  
Clustering Methods ◽  
Fuzzy Clustering Method ◽  
Communication Devices ◽  
Fuzzy Clustering Methods

The use of mobile communication devices in health care is spreading worldwide. A huge amount of health data collected by these devices (mobile health data) is nowadays available. Mobile health data may allow for real-time monitoring of patients and delivering ad-hoc treatment recommendations. This paper aims at showing how this may be done by exploiting the potentialities of fuzzy clustering techniques. In fact, such techniques can be fruitfully applied to mobile health data in order to identify clusters of patients for diagnostic classification and cluster-specific therapies. However, since mobile health data are full of noise, fuzzy clustering methods cannot be directly applied to mobile health data. Such data must be denoised prior to analyzing them. When longitudinal mobile health data are available, functional data analysis represents a powerful tool for filtering out the noise in the data. Fuzzy clustering methods for functional data can then be used to determine groups of patients. In this work we develop a fuzzy clustering method, based on the concept of medoid, for functional data and we apply it to longitudinal mHealth data on daily symptoms and consumptions of anti-symptomatic drugs collected by two sets of patients in Berlin (Germany) and Ascoli Piceno (Italy) suffering from allergic rhinoconjunctivitis. The studies showed that clusters of patients with similar changes in symptoms were identified opening the possibility of precision medicine.

Power-Regularized Fuzzy Clustering for Spherical Data

2018 ◽  
Vol 22 (2) ◽  
pp. 163-171
Author(s):  
Yuchi Kanzawa ◽  
Keyword(s):  
Fuzzy Clustering ◽  
Numerical Experiments ◽  
Clustering Methods ◽  
Clustering Method ◽  
Spherical Data ◽  
Fuzzy Clustering Method ◽  
Fuzzy Clustering Methods ◽  
Artificial Datasets

In this paper, a power-regularization-based fuzzy clustering method is proposed for spherical data. Power regularization has not been previously applied to fuzzy clustering for spherical data. The proposed method is transformed to the conventional fuzzy clustering method, entropy-regularized fuzzy clustering for spherical data (eFCS), for a specified fuzzification parameter value. Numerical experiments on two artificial datasets reveal the properties of the proposed method. Furthermore, numerical experiments on four real datasets indicate that this method is more accurate than the conventional fuzzy clustering methods: standard fuzzy clustering for spherical data (sFCS) and eFCS.

ON A MULTICRITERIA FUZZY CLUSTERING METHOD FOR 3-WAY DATA

1994 ◽  
Vol 02 (02) ◽  
pp. 127-142 ◽  
Author(s):  
MIKA SATO ◽  
YOSHIHARU SATO
Keyword(s):  
Fuzzy Clustering ◽  
Multicriteria Optimization ◽  
Optimization Problem ◽  
Combinatorial Problem ◽  
Efficient Solutions ◽  
Clustering Problem ◽  
Fuzzy Clustering Method ◽  
Hard Clustering ◽  
Pareto Efficient ◽  
Multicriteria Optimization Problem

In this paper, we discuss the fuzzy clustering for 3-way data which is composed of objects, attributes and situations. In the case of 2-way data, the clustering of objects are usually based on the values of attributes. However, for 3-way data, a clustering of objects is not always coincident for all situations, because the values of attributes vary with each situation. Therefore, the clustering problem for 3-way data can be regarded as a multicriteria optimization problem. It is known that the practical solutions for a multicriteria optimization problem are Pareto efficient solutions. In a multicriteria hard (non-fuzzy) clustering problem, it is difficult to find a Pareto efficient cluster since this is essentially combinatorial problem. But we show that a multicriteria fuzzy clustering, which is an extension of this hard clustering problem, has merit to obtain Pareto efficient clusters. We investigate the features for Pareto efficient clusters using an artificial 3-way data and we show a numerical example applied to the data of the growth of physical constitutions.

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