scholarly journals Fuzzy Entropy-Based Spatial Hotspot Reliability

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 531
Author(s):  
Ferdinando Di Martino ◽  
Salvatore Sessa
Keyword(s):  
Clustering Algorithm ◽  
Geographical Area ◽  
Fuzzy Entropy ◽  
Circular Area ◽  
Analysis Problem ◽  
Trade Off ◽  
Fuzzy C Means ◽  
Good Trade ◽  
Disease Analysis ◽  
Fuzzy C Means Algorithm

Cluster techniques are used in hotspot spatial analysis to detect hotspots as areas on the map; an extension of the Fuzzy C-means that the clustering algorithm has been applied to locate hotspots on the map as circular areas; it represents a good trade-off between the accuracy in the detection of the hotspot shape and the computational complexity. However, this method does not measure the reliability of the detected hotspots and therefore does not allow us to evaluate how reliable the identification of a hotspot of a circular area corresponding to the detected cluster is; a measure of the reliability of hotspots is crucial for the decision maker to assess the need for action on the area circumscribed by the hotspots. We propose a method based on the use of De Luca and Termini’s Fuzzy Entropy that uses this extension of the Fuzzy C-means algorithm and measures the reliability of detected hotspots. We test our method in a disease analysis problem in which hotspots corresponding to areas where most oto-laryngo-pharyngeal patients reside, within a geographical area constituted by the province of Naples, Italy, are detected as circular areas. The results show a dependency between the reliability and fluctuation of the values of the degrees of belonging to the hotspots.

scholarly journals A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 554 ◽  
Author(s):  
Barbara Cardone ◽  
Ferdinando Di Martino
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Fuzzy Entropy ◽  
Entropy Function ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Random Initialization ◽  
Fuzzy C Means Algorithm ◽  
Optimal Cluster

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.

Management of Abstract Algebra Concepts Based on Knowledge Structure

2013 ◽  
Vol 284-287 ◽  
pp. 3537-3542
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih
Keyword(s):  
Mahalanobis Distance ◽  
Clustering Algorithm ◽  
Fuzzy Theory ◽  
Clustering Algorithms ◽  
Knowledge Structures ◽  
Interpretive Structural Modeling ◽  
Fuzzy C Means ◽  
Integrated Method ◽  
Combined Algorithm ◽  
Fuzzy C Means Algorithm

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

Application of Hard C-Means and Fuzzy C-Means in Data Fusion

2012 ◽  
Vol 190-191 ◽  
pp. 265-268
Author(s):  
Ai Hong Tang ◽  
Lian Cai ◽  
You Mei Zhang
Keyword(s):  
Data Fusion ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Large Data ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Data Similarity ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Algorithm

This article describes two kinds of Fuzzy clustering algorithm based on partition,Fuzzy C-means algorithm is on the basis of the hard C-means algorithm, and get a big improvement, making large data similarity as far as possible together. As a result of Simulation, FCM algorithm has more reasonable than HCM method on convergence, data fusion, and so on.

scholarly journals An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Elid Rubio ◽  
Oscar Castillo ◽  
Fevrier Valdez ◽  
Patricia Melin ◽  
Claudia I. Gonzalez ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Clustering Algorithm ◽  
Image Preprocessing ◽  
Cover Type ◽  
Fuzzy C Means ◽  
Possibilistic Clustering ◽  
Interval Type ◽  
Fuzzy C Means Algorithm

In this work an extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM) algorithm to observe if the proposed approach has better performance than this algorithm.

Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization

2015 ◽  
Vol 23 (05) ◽  
pp. 667-683 ◽  
Author(s):  
Troudi Ahmed ◽  
Bouzbida Mohamed ◽  
Chaari Abdelkader
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Kernel Method ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi–Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global optimization technique. Thus, the incorporation of local research capacity of the KNEPCM algorithm and the global optimization ability of the PSO algorithm can solve these problems. In this paper, a new clustering algorithm called KNEPCM-PSO is proposed. This algorithm is a combination between Kernel New Extended Possibilistic C-Means algorithm (KNEPCM) and Particle Swarm Optimization (PSO). The effectiveness of this algorithm is tested on nonlinear systems and on an electro-hydraulic system.

FUZZY CLUSTERING WITH GENETICALLY ADAPTIVE SCALING

2002 ◽  
Vol 02 (04) ◽  
pp. 557-572 ◽  
Author(s):  
ADAM SCHENKER ◽  
MARK LAST ◽  
HORST BUNKE ◽  
ABRAHAM KANDEL
Keyword(s):  
Clustering Algorithm ◽  
Synthetic Data ◽  
Data Set ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Optimal Values ◽  
The Given ◽  
Fuzzy C Means Algorithm ◽  
Incorrect Classification ◽  
Iris Data

In this paper we present a genetically enhanced version of the classical fuzzy c-means clustering algorithm. Our algorithm uses an evolutionary method to find optimal values for some scaling constants which are used to scale the various dimensions of the given data set so that clusters can be more easily detected by compensating for differences in distributions among features. We demonstrate how using un-scaled data with the conventional fuzzy c-means algorithm can lead to incorrect classification and how our algorithm overcomes the problem. We present the results of applying our method to both a synthetic data set, which we created to demonstrate the problem, and the standard Iris data set. In both cases, reduction of misclassifications was obtained by the new method, demonstrating improvement over the standard fuzzy c-means algorithm.

scholarly journals Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance

2012 ◽  
Vol 1 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Ahmad Sanmorino
Keyword(s):  
Clustering Algorithm ◽  
Staining Technique ◽  
Image Clustering ◽  
Fuzzy C Means ◽  
Average Value ◽  
Artistic Value ◽  
Fuzzy Models ◽  
Average Luminance ◽  
Fcm Clustering ◽  
Fuzzy C Means Algorithm

Batik is a fabric or clothes that are made ​​with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM) algorithm based on log-average luminance of the batik. FCM clustering algorithm is an algorithm that works using fuzzy models that allow all data from all cluster members are formed with different degrees of membership between 0 and 1. Log-average luminance (LAL) is the average value of the lighting in an image. We can compare different image lighting from one image to another using LAL. From the experiments that have been made, it can be concluded that fuzzy c-means algorithm can be used for batik image clustering based on log-average luminance of each image possessed.DOI: 10.18495/comengapp.11.025031

scholarly journals The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency

2018 ◽  
Vol 5 (2) ◽  
pp. 194-204
Author(s):  
Feroza Rosalina Devi ◽  
Endang Sugiharti ◽  
Riza Arifudin
Keyword(s):  
Beef Cattle ◽  
Clustering Algorithm ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Fuzzy C Means ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification ◽  
Fuzzy C Means Algorithm

The beef cattle quality certainly affects the quality of meat to be consumed. This researchperforms data processing to do the classification of beef cattle quality. The data used are196 data record taken from data in 2016 and 2017. The data have 3 variables fordetermining the quality of beef cattle in Semarang regency namely age (month), Weight(Kg), and Body Condition Score (BCS) . In this research, used the combination of NaïveBayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification andK-Means. After doing the combinations, then conducted analysis of the results of whichtype of combination that has a high accuracy. The results of this research indicate that theaccuracy of combination Naïve Bayes Classification and K-Means has a higher accuracythan the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seenfrom the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifierof 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithmis 98,33%, so it can be concluded that combination of K Means Clustering algorithm andNaïve Bayes Classifier is more recommended for determining the quality of beef cattle inSemarang regency.

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