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A Survey on Clustering Algorithms for Partitioning Method

Hoda Khanali, Babak Vaziri
2016 International Journal of Computer Applications  
Clustering is one of the data mining methods. In all clustering algorithms, the goal is to minimize intracluster distances, and to maximize intercluster distances.  ...  Comparing various methods of the clustering, the contributions of the recent researches focused on solving the clustering challenges of the partition method.  ...  Because the type-2 fuzzy approach has high computational complexity, it affects the high computational time, and the high iterations of IT2CC and Multi-central general type-2 fuzzy clustering algorithms  ... 
doi:10.5120/ijca2016912291 fatcat:apq7vblpmbdovknveptvbyabnm

Qualitative primitive identification using fuzzy clustering and invariant approach

Y.Y. Cai, H.T. Loh, A.Y.C. Nee
1996 Image and Vision Computing  
The fuzzy C-shell clustering technique is applied to partition range images into a set of quadric shells.  ...  Using fuzzy shell clustering, the shell features can be segmented and fitted simultaneously, and individual best-fitted shells can be clustered concurrently.  ...  Shiraishi of Ibaraki University for the helpful discussions. Thanks also go to the anonymous reviewers for their valuable suggestions and comments.  ... 
doi:10.1016/0262-8856(95)01053-x fatcat:p3exu67qvracdng3f55432k3qi

Fuzzy human motion analysis: A review

Chern Hong Lim, Ekta Vats, Chee Seng Chan
2015 Pattern Recognition  
In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives.  ...  To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA.  ...  A comprehensive review on handling the uncertainty in pattern recognition using the type-2 fuzzy approach was provided by [181] .  ... 
doi:10.1016/j.patcog.2014.11.016 fatcat:oreztincyfbblkmo77lolqpiam

Computational Intelligence Approaches to Brain Signal Pattern Recognition [chapter]

Pawel Herman, Girijesh Prasad, Thomas Martin
2008 Pattern Recognition Techniques, Technology and Applications  
MI induced EEG patterns in a multi-session setup for multiple subjects.  ...  Computational intelligence in pattern recognition As discussed earlier, the uncertainty effects inherent to brain signal pattern recognition have a multi-faceted nature.  ...  Computational Intelligence Approaches to Brain Signal Pattern Recognition, Pattern Recognition Techniques, Technology and Applications, Peng-Yeng Yin (Ed.), ISBN: 978-953-7619-24-4, InTech, Available from  ... 
doi:10.5772/6238 fatcat:nveopxtjrbgvzdc7thscvqye4m

A Hybrid Approach of Fuzzy C-mean Clustering and Genetic Algorithm (GA) to Improve Intrusion Detection Rate

2015 International Journal of Science and Research (IJSR)  
We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection.  ...  This paper proposes genetic algorithm and fuzzy c-means clustering to generate to detect intrusions.The goal of intrusion detection is to monitor network activities automatically, detect malicious attacks  ...  This method is frequently used in pattern recognition.  ... 
doi:10.21275/v5i5.nov163546 fatcat:t5dtgahidzad5mpxinzhsllody

Simple and Computationally Efficient Movement Classification Approach for EMG-controlled Prosthetic Hand: ANFIS vs. Artificial Neural Network

Hessam Jahani Fariman, Siti A. Ahmad, M. Hamiruce Marhaban, M. Ali Jan Ghasab, Paul H. Chappell
2015 Intelligent Automation and Soft Computing  
The fuzzy C-mean clustering method and 23 scatter plots were used to evaluate the performance of the proposed multi-feature versus other 24 accurate multi-features.  ...  The 20 surface myoelectric signals were acquired from 2 muscles -Flexor Carpi Ulnaris and Extensor Carpi 21 Radialis of 4 normal-limb subjects.  ...  Other similar neuro-fuzzy approaches, 338 such as those in (Khezri & Jahed, 2007) and (Favieiro & Balbinot, 2011), Scatter plot of the Hudgins's multi-feature as feature extractor for one subject, 2 channels  ... 
doi:10.1080/10798587.2015.1008735 fatcat:bc54hzag65glropicoyqn22ifa

Analysis and Detection of Multi Tumor from MRI of Brain using Advance Adaptive Feature Fuzzy C-means (AAFFCM) Algorithm

B. Srikanth, E. Srineevasa Reddy
2016 Indian Journal of Science and Technology  
Findings: The proposed AAFFCM approach is a hybrid approach which is a combination of fuzzy c-means and SVM algorithms for detecting multi-tumors in brain.  ...  The present approach derives an innovative method for brain tumor analysis and detection based on the support vector machine (SVM) and fuzzy c-means algorithms..  ...  Comparison events for example detachment, connectivity, and concentration are used. Its request is in data analysis, pattern recognition and image segment 22 . Figure 2.  ... 
doi:10.17485/ijst/2016/v9i43/100204 fatcat:5ciirfeicbdadkajcmrlblps6q

Driving Style Recognition Using Interval Type-2 Fuzzy Inference System and Multiple Experts Decision Making [article]

Iago Pachêco Gomes, Denis Fernando Wolf
2022 arXiv   pre-print
Therefore, this paper presents a driving style recognition using Interval Type-2 Fuzzy Inference System with Multiple Experts Decision-Making for classifying drivers into calm, moderate and aggressive.  ...  The proposed approach was evaluated using descriptive statistics analysis, and compared with clustering algorithms and a type-1 fuzzy inference system.  ...  We thank the Coordination for the Improvement of Higher Education Personnel -Brazil (CAPES) for the financial support under grant 88887.500344/2020-0, the São Paulo Research Foundation (FAPESP) for the  ... 
arXiv:2110.13805v2 fatcat:oonc6iupxrd2dbq3i7w4bjsk4y

Fuzzy and Neural Network based Tomato Plant Disease Classification using Natural Outdoor Images

Hiteshwari Sabrol, Satish Kumar
2016 Indian Journal of Science and Technology  
For classification, computed features are fed into three classifiers, i.e., "Fuzzy Inference System based on subtractive clustering", "Adaptive neuro-fuzzy inference system using hybrid learning algorithm  ...  Novelty/Improvement: Usually, in the studies the only one type of plant disease considered for the recognition and classification purpose.  ...  ., for guidance and sharing his expertise in the field of plant pathology. We would also like to thanks, Mr.  ... 
doi:10.17485/ijst/2016/v9i44/92825 fatcat:rm47ec7vr5ecbjnywq4fgji6ru

A Research on Different Clustering Algorithms and Techniques

M. Pavithra, P. Nandhini, R. Suganya
2018 International Journal of Trend in Scientific Research and Development  
Whatever a clustering algorithm provides a better performance, it has the more successful to achieve this goal [2] .  ...  This survey mainly focuses on partition based clustering algorithms namely k k-Medoids and Fuzzy c-Means In particular; they applied mostly in medical data sets.  ...  Because the type-2 fuzzy approach has high computational complexity, it affects the high computational time, and the high iterations of IT2CC and Multi-central general type-2 fuzzy clustering algorithms  ... 
doi:10.31142/ijtsrd15899 fatcat:g6gbugoa7ze3rg3cggscjwshy4

Color Textured Image Segmentation Using ICICM - Interval Type-2 Fuzzy C-Means Clustering Hybrid Approach

Murugeswari Palanivel, Manimegalai Duraisamy
2012 Engineering Journal  
Then, Extended Interval Type-2 Fuzzy C-means clustering algorithm is used to cluster the obtained feature vectors into several classes corresponding to the different regions of the textured image.  ...  Segmentation is an essential process in image processing because of its wild application such as image analysis, medical image analysis and pattern recognition.  ...  Acknowledgment The authors would like to acknowledge the Tijuana Institute of Technology and Baja California Autonomous University, Tijuana Campus, Mexico for providing the Interval Type-2 Fuzzy toolbox  ... 
doi:10.4186/ej.2012.16.5.115 fatcat:cpurbbmmizhmnh2cubiiffy72m

Combining neural networks for Arabic handwriting recognition

Chergui Leila, Kef Maamar, Chikhi Salim
2011 2011 10th International Symposium on Programming and Systems  
The MCS combines three neuronal recognition systems based on Fuzzy ART network used for the first time in Arabic OCR, multi layer perceptron and radial basic functions.  ...  In this paper we present a Multiple Classifier System (MCS) for off-line Arabic handwriting recognition.  ...  It is similar to many iterative clustering algorithms where each pattern is processed by finding the nearest cluster and then updating that cluster to be closer to the pattern.  ... 
doi:10.1109/isps.2011.5898872 fatcat:3dvseafbcnb4tbwf4efegkbtbe

Automatic real-time road marking recognition using a feature driven approach

Alireza Kheyrollahi, Toby P. Breckon
2010 Machine Vision and Applications  
Here we propose an approach to this problem based on the extraction of robust road marking features via a novel pipeline of inverse perspective mapping and multi-level binarisation.  ...  The approach is shown to operate successfully over a range of lighting, weather and road surface conditions.  ...  Overall we see 85% successful recognition for arrows and 81% recognition for the 19 dictionary text patterns/words.  ... 
doi:10.1007/s00138-010-0289-5 fatcat:7ndfsvntrfhtjj6twfnc5ixc64

Hand movements classification for myoelectric control system using adaptive resonance theory

H. Jahani Fariman, Siti A. Ahmad, M. Hamiruce Marhaban, M. Alijan Ghasab, Paul H. Chappell
2015 Australasian physical & engineering sciences in medicine  
Fuzzy C-means clustering method and scatter plot were used to 21 evaluate the performance of the proposed multi-feature versus Hudgins' multi-feature.  ...  The typical approach to myoelectric control is to use a pattern 45 recognition scheme [4].  ...  In the same character recognition tests referred to in the previous 98 section, Xu The current study intends to propose a pattern-recognition approach for the classification of 105 hand movements in  ... 
doi:10.1007/s13246-015-0399-5 pmid:26581764 fatcat:k6jybiu7mncw3pjuuivawvyzvq

On the Uniform Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Time-Varying Environment [chapter]

Meng Joo Er, Piotr Duda
2012 Lecture Notes in Computer Science  
Starczewski Slawomir Jaszczak and Joanna Kolodziejczyk Implications on Ordered Fuzzy Numbers and Fuzzy Sets of Type Two 247 Fuzzy Clustering of Intuitionistic Fuzzy Data 213 Bohdan S.  ...  Olgierd JJnold. and Pawel Skrobanek Foundations of Rough Biclustering 144 Marcin Michalak ORG -Oblique Rules Generator 152 Marcin Plucinski A Cluster Validity Index for Hard Clustering 168  ... 
doi:10.1007/978-3-642-29347-4_5 fatcat:pqwbzeuqsbg6lkhlmmrsj6qb24
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