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Word Embeddings and Validity Indexes in Fuzzy Clustering
[article]
2022
arXiv
pre-print
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Also we introduce new methods of fuzzy clustering based on hybrid implementation of fuzzy clustering methods with an evolutionary algorithm named Forest Optimization. ...
The results indicate that fuzzy clustering algorithms are very sensitive to high-dimensional data, and parameter tuning can dramatically change their performance. ...
Our study evaluates 4 method of clustering: Fuzzy C-Mean, Fuzzy Gustafson-Kessel, FOA Fuzzy C-Mean witch is hybrid implementation of Fuzzy C-Mean and Forest Optimization, and FOA Gustafson-Kessel which ...
arXiv:2205.06802v1
fatcat:7e755ztnnrgetierk4mzvta3ee
A Data Transmission Scheme Using K-Means and Fuzzy Logic for IOT Sensor Based Forest Fire Detection System
2022
International Journal of Emerging Trends in Engineering Research
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A Fuzzy-based Cluster Head selection technique for WSN in detecting forest fire is presented. ...
In the proposed scheme, the nodes are divided into clusters using K-means clustering and then the cluster heads are determined by using a fuzzy logic scheme. ...
A typical ISN is made up of hundreds or even thousands of sensor nodes that
A Data Transmission Scheme Using K-Means and Fuzzy Logic for IOT Sensor Based Forest Fire Detection System Asan Abbas Sadeq ...
doi:10.30534/ijeter/2022/0110102022
fatcat:i27w5prpqfhrrjz52ohrnqyhjq
Enhanced Diabetic Prediction Using Fuzzy C-Means Preprocessing and Random Forest Ensemble Learning
2023
VFAST Transactions on Software Engineering
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Despite the adaptability of fuzzy C-Means for various data types, the ultimate outcome of the clustering process hinges on the initial placement of cluster centers. ...
Our principal objective was to enhance the accuracy of fuzzy C-means clustering and the random forest. ...
Saood Zia, for their invaluable guidance. Special gratitude to our dedicated research team whose contributions were crucial to the project's success. ...
doi:10.21015/vtse.v11i4.1657
fatcat:3qvtmwfzpva2dn4mi3e2ipm7py
FRC: A New Dynamic Fuzzy Clustering Alogorithm Based on Random Forest Algorithm
2018
Journal of clean energy technologies
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The main defect of traditional methods of fuzzy clustering is to know the number of clustering in advance. The text eigenvector is acquired based on the vector space model (VSM) and TF.IDF method. ...
The fuzzy clustering algorithm is suitable for dealing with the semantic variety and complexity. The example demonstrates the effectiveness of the present algorithm. ...
FRFC algorithm is united with random forest and fuzzy clustering, which overcomes traditional fuzzy clustering base on partition is to know the number of clustering in advance. ...
doi:10.7763/ijcte.2018.v10.1224
fatcat:m34rkv6hhncwfmuwzxmgnj6woq
Fuzzy Random Forest with C–Fuzzy Decision Trees
[chapter]
2016
Lecture Notes in Computer Science
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In this paper a new classification solution which joins C-Fuzzy Decision Trees and Fuzzy Random Forest is proposed. ...
Its assumptions are similar to the Fuzzy Random Forest, but instead of fuzzy trees it consists of C-Fuzzy Decision Trees. To test the proposed classifier there was performed a set of experiments. ...
Constructing clusters and grouping objects into them is based on Fuzzy C-Means technique (FCM), which is is an example of a fuzzy clustering. ...
doi:10.1007/978-3-319-45378-1_43
fatcat:nzhi7542wfb67ij6f33wfybphe
Application of Clustering Analysis in Brain Gene Data Based on Deep Learning
2019
IEEE Access
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INDEX TERMS Deep belief network, fuzzy c-means algorithm, unsupervised learning, brain gene data clustering. ...
Then, a clustering model based on deep learning is proposed, and a clustering algorithm is implemented by using deep belief network (DBN) and fuzzy c-means algorithm (FCM). ...
After preprocessing the selected brain gene database data, a fuzzy c-means clustering model based on deep neural network is established. ...
doi:10.1109/access.2018.2886425
fatcat:23bdpd22m5ezjheilmhpmsoqby
Fuzzy clustering based on Forest optimization algorithm
2018
Journal of King Saud University: Computer and Information Sciences
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Clustering is one of the classification methods for data analysis and it is one of the ways of data analysis, too. ...
By analyzing and comparing the results of the proposed method with the results of algorithms GGAFCM (fuzzy clustering based on genetic algorithm) and PSOFCM (fuzzy clustering based on particle swarm optimization ...
Bezdek developed a fuzzy clustering algorithm, the wellknown fuzzy c-means (FCM) (Bezdek, 1973a) . The algorithm is the fuzzy equivalence of the algorithm k-means. ...
doi:10.1016/j.jksuci.2016.09.005
fatcat:4qlm6irwvralzgnkeuqystewva
ANALYSIS OF CROP YIELD PREDICTION USING FUZZY CLUSTERING TECHNIQUES
2020
International Journal of Advanced Research in Computer Science
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Fuzzy C-Means(FCM), Fuzzy logic (FL), Adaptive Neuro Fuzzy Inference System(ANFIS),Multiple Linear Regression(MLR), Linear Discriminant Analysis (LDA) are used to survey out high accuracy and less error ...
Data mining have modern techniques and algorithms for finding best yield prediction. ...
1] [2] , Possibilistic C-Means (PCM) [3] [2] , Fuzzy Possibilistic C-Means (FPCM) [4] , and Possibilistic Fuzzy Means (PFCM) [5] [2] which is widely used technique for crop yield prediction in ...
doi:10.26483/ijarcs.v11i6.6671
fatcat:3jprprlqkrbxvh3vej3ufeha2y
A Hybrid Distance-Based and Naive Bayes Online Classifier
[chapter]
2015
Lecture Notes in Computer Science
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The paper combines distance-based weak classifiers constructed using kernel fuzzy clustering technique with the naive Bayes algorithm. ...
Resulting hybrid online ensemble is validated through computational experiment involving a number of datasets often used for testing data streams mining algorithms. ...
fuzzy C-means clustering with centroids in kernel space. ...
doi:10.1007/978-3-319-24306-1_21
fatcat:s4bqftxs4fdcnmzidy5r3dnege
Drought-prone areas mapping using fuzzy c-means method in Gunungkidul district
2021
Pythagoras: Jurnal pendidikan Matematika
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The purpose of this study is to map drought-prone areas in Gunungkidul district using the fuzzy c-means method, making it easier for the government to allocate water-dropping assistance to drought-affected ...
The results of fuzzy c-means clustering revealed three clusters with a low level of vulnerability consisting of 7 sub-districts, a moderate level of vulnerability consisting of 8 sub-districts, and a high ...
Agency for providing 2016 slope data, and the Land and Spatial Planning Agency of the Special Region of Yogyakarta for providing valid maps for 2018-2038 and SHP files of rainfall, soil types, infiltration ...
doi:10.21831/pythagoras.v16i2.43780
fatcat:opc6kzydzffmta7wq4rhduyxge
Synthetic Aperture Radar (SAR) image segmentation by fuzzy c-means clustering technique with thresholding for iceberg images
2014
Computational Ecology and Software
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Fuzzy c-means (FCM) clustering algorithm is widely used for image segmentation. ...
The purpose of clustering is to identify natural groupings of data from a large data set, which results in concise representation of system's behavior. ...
Lopez from Sandia National Laboratories Albuquerque, NM, USA, Ellen O' Leary from Radar Data Center, Jet Propulsion Laboratory Pasadena, CA and J.C Bezdek for facilitating theoretical concepts, and to ...
doaj:f97d4e16f4644b5a80debd669888d233
fatcat:ggth7pkjnbbx7fdww563h53dgi
The Application on Intrusion Detection Based on K-means Cluster Algorithm
2009
2009 International Forum on Information Technology and Applications
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Keywords-Feature selection, k-mean clustering, fuzzy k mean clustering, Random Forest, and KDDcup 99 dataset I. ...
From experimental results it is observed that for 2 class datasets filtered fuzzy random forest dataset gives the better results. ...
Membership of training datasets is calculated by fuzzy c mean clustering and for test dataset use the same centroid as used in training datasets. ...
doi:10.1109/ifita.2009.34
fatcat:g6ahsm77mnf2rpz5yghoqyckvy
Predicting Size of Forest Fire Using Hybrid Model
[chapter]
2014
Lecture Notes in Computer Science
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The hybrid model is developed with clustering and classification approaches. Fuzzy C-Means (FCM) is used to cluster the historical variables. ...
The label dataset having value greater than zero in fire area size are clustered using FCM to produce two categorical clusters,i.e.: Light Burn, and Heavy Burn for its label. ...
Fuzzy C-Means here will cluster the data based on eight Meteorological variables. ...
doi:10.1007/978-3-642-55032-4_31
fatcat:hplfhdi47jf3fotkfwxhw75ol4
Comparative Analysis of Incomplete Business Data Clustering
2022
Highlights in Science Engineering and Technology
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It means that a common way to deal with missing data is to delete the sample that contains the missing attribute. ...
This paper compares six benchmark business datasets by adopting several different data imputation methods and supplementing the missing data with a clustering approach (unsupervised learning). ...
The fuzzy c-means clustering algorithm Fuzzy C-means Algorithm (FCMA or FCM). ...
doi:10.54097/hset.v22i.3294
fatcat:uuohpata4zd77gwhp2ivxg3mri
PREDICTION OF FOREST FIRE USING NEURAL NETWORKS WITH BACKPROPAGATION LEARNING AND EXREME LEARNING MACHINE APPROACH USING METEOROLOGICAL AND WEATHER INDEX VARIABLES
2021
Media statistika
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In this study, we discuss the method for prediction of the size of the forest fire using the hybrid approach between Fuzzy-C-Means clustering (FCM) and Neural Networks (NN) classification with backpropagation ...
We found that the best approach will be obtained using hybrid FCM-SVM for data training, where the best performance obtains for hybrid FCM-NN-backpropagation for data testing. ...
Clustering Method: Fuzzy C-Means Clustering The Fuzzy C-Means (FCM) clustering (see e.g., Bezdek, 1981) is an extension of the classical k-mean clustering approach. ...
doi:10.14710/medstat.14.2.118-124
fatcat:w7ovcgelsjgfhmox4utwajougi
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