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A Self-Adaptive Weighted fuzzy c-means for Mixed-Type Data

INTERNATIONAL JOURNAL OF cOMPUTATIONAL INTELLIGENcE AND APPLIcATIONS 2020年第4期19卷

作者： Ren, Min Wang, Zhihao Yang, Guangfen Shandong Univ Finance & Econ Sch Math & Quantitat Econ Jinan Shandong Peoples R China Shandong Univ Finance & Econ Sch Management & Engn Jinan Shandong Peoples R China

The influence of features on each cluster is not the same in a mixed-type dataset. Based on the rough set and shadow set theories, the fuzzy distribution centroid was defined to represent the clustering center of the discrete feature so that the fuzzy c-means algorithm (FcM) could be extended to cluster the data with both continuous and discrete features. Then, considering the different contributions of the features to each cluster, a new weighted objective function was constructed in accordance with the principles of fuzzy compactness and separation. Because the learning feature weight is the key step in feature-weighted FcM, this paper regarded the feature weight as a variable optimized in the clustering process and put forward a self-adaptive mixed-type weighted FcM. The experimental results showed that the algorithm could be effectively applied to a heterogeneous mixed-type dataset.

关键词： fuzzy c-means algorithm weighted objective function rough set shadow set

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Mapping Student Performance With Employment Using fuzzy c-means

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INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2020年第4期11卷 36-52页

作者： Singh, Ishwank Sabitha, Sai choudhury, Tanupriya Aggarwal, Archit Dewangan, Bhupesh Kumar Amity Univ Noida India Univ Petr & Energy Studies Dehra Dun Uttarakhand India Univ Petr & Energy Studies Dept Informat Dehra Dun Uttarakhand India

Technical organisations are ranked based on performance indicators like resources, students' intake, global reputation, and research activities. Student performance and placement are important factors in deciding the ranking of a university. Student performance analysis is a recent and widely researched domain aimed at reforming the education system. The analysis assists institutions to understand and improve their performance and educational outcomes. Admissions, academics, and placement are the three most significant processes during which the large amount of data is gathered within a university and there is a requirement of analysis. The data mining techniques are used for data analysis processes and it encompasses data understanding, pre-processing, modelling, and implementation. In this research work, fuzzy c-means clustering technique is used to understand fuzziness of student performance, classify and map the student performance to employability. To understand this objective, the dataset has been collected from universities, pre-processed, and analysed.

关键词： cluster Analysis Data Mining fuzzy c-means algorithm Overall Performance Silhouette Measure Student Performance

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Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and fuzzy c-means Optimization

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JOURNAL OF KING SAUD UNIVERSITY-cOMPUTER AND INFORMATION ScIENcES 2022年第3期34卷 557-566页

作者： Sheela, c. Jaspin Jeba Suganthi, G. Manonmaniam Sunadaranar Univ St Xaviers Autonomous Coll Tirunelveli 627012 Tamil Nadu India Manonmaniam Sunadaranar Univ WC Coll Dept Comp Sci Tirunelveli 627012 Tamil Nadu India

The automatic brain tumor segmentation in MRI (Magnetic Resonance Images) is becoming a challenging task in the field of medicine, since the brain tumor occurs in different shapes, intensities and sizes. This paper proposes an efficient automatic brain tumor segmentation using Greedy Snake Model and fuzzy c-means optimization. This method initially identifies the approximate Region Of Interest (ROI), by removing the non-tumor part by two level morphological reconstruction such as dilation and erosion. A mask is formed by thresholding the reconstructed image and is eroded to improve the accuracy of segmentation in Greedy Snake algorithm. Using the mask boundary as initial contour of the snake, the greedy snake model estimates the new boundaries of tumor. These boundaries are accurate in regions where there is sharp edge and are less accurate where there are ramp edges. The inaccurate boundaries are further optimized by using fuzzy c-means algorithm to obtain the accurate segmentation output. The region that has large perimeter is finally chosen, to eliminate the in-accurate segmented regions. The experimental verification were done on T-1-weighted contrast-enhanced image data set, using the metrics such as dice score, specificity, sensitivity and Hausdorff distance. The proposed method outperforms when compared with the traditional brain tumor segmentation methods in MRI images. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the cc BY-Nc-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： MRI images Image segmentation fuzzy c-means algorithm Greedy snake model

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OPTIMIZING ENERGY cONSUMPTION IN VEHIcULAR SENSOR NETWORKS BY cLUSTERING USING fuzzy c-means AND fuzzy SUBTRAcTIVE algorithmS 2

OPTIMIZING ENERGY CONSUMPTION IN VEHICULAR SENSOR NETWORKS B...

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ISPRS Int Joint conf of 2nd conf on Geospatial Information Research GI Research, 4th conf on Sensors and Models in Photogrammetry and Remote Sensing (SMPR), 6th conf on Earth Observation of Environmental changes (EOEc)

作者： Ebrahimi, Aref Pahlavani, Parham Masoumi, Zohreh Univ Tehran Coll Engn Sch Surveying & Geospatial Engn GIS Div Tehran Iran Univ Tehran Coll Engn Ctr Excellence Geomat Engn Disaster Management Sch Surveying & Geospatial Engn Tehran Iran Inst Adv Studies Basic Sci Dept Earth Sci Zanjan Iran

Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMc) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs);moreover, it is the most important feature in designing these networks. clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a cluster Head (cH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, fuzzy c-means (FcM) and fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FcM algorithm and fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. comparing the performance of the algorithms implies the 1.5 percent improvement in fuzzy Subtractive algorithm in comparison.

关键词： Wireless Sensor Networks Traffic Information Systems Optimization clustering Energy consumption clustering fuzzy c-means algorithm fuzzy Subtractive algorithm

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Extraction of Retinal Blood Vessel using curvelet Transform and fuzzy c-means 22

Extraction of Retinal Blood Vessel using Curvelet Transform ...

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22nd International conference on Pattern Recognition (IcPR)

作者： Kar, Sudeshna Sil Maity, Santi P. Indian Inst Engn Sci & Technol Dept Informat Technol Howrah 711103 WB India

ISBN: (纸本)9781479952083

This paper addresses the automatic blood vessel detection problem in retinal images using matched filtering in an integrated system design platform that involves curvelet transform and fuzzy c-means. Although noise is kept constant in medical ccD cameras, due to a number of factors, the contrast between the background and the blood vessels in retinal images and consequently the visual quality of the images looks very poor. Some form of pre-processing operation is therefore essential for the accurate extraction of these blood vessels. Since curvelet transform can represent lines, edges and curvatures very well as compared to other multi-resolution techniques, this paper uses curvelet transform to enhance the retinal vasculature. Matched filtering is then used to intensify the blood vessels which is further employed by fuzzy c-means algorithm to extract the vessel silhouette from the background. Performance is evaluated on publicly available DRIVE database and is compared with the existing blood vessel extraction methodology that uses curvelet transform. Simulation results demonstrate that the proposed method is very much efficient in detecting long and thick as well as short and thin vessels, wherein the existing methods fail to extract tiny and thin vessels.

关键词： Retinal image segmentation Vessel detection curvelet transform Matched filter fuzzy c-means algorithm

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NEW FEATURES USING fuzzy c-means ALOGORITHM FOR AUTOMATIc LANGUAGE REcOGNITION 5

NEW FEATURES USING FUZZY C-MEANS ALOGORITHM FOR AUTOMATIC LA...

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5th IEEE International conference on computational Intelligence and computing Research (IEEE IccIc)

作者： Sadanandam, M. Prasad, V. Kamakshi Ramana, N. Rao, E. Jagadeshwara Kakatiya Univ CSE Warangal 506009 Telangana India JNTUH CSE Hyderabad 500072 Telangana India

ISBN: (纸本)9781479939756

We propose new features for the language recognition using Gaussian computations. New features are derived from traditional features like Mel frequency cepstral coefficients (MFcc) using fuzzy c-means clustering algorithm. MFcc feature vectors derived from huge corpus of all languages under consideration are grouped into c-clusters using fuzzy c-means clustering algorithm and one Gaussian distribution is modeled for each cluster. In the training phase, new feature vectors are derived from language specific speech corpus using the clusters which are formed by fuzzy c-means clustering algorithm. In the testing phase, similar procedure is followed for the extraction of c-element feature vectors from unknown speech utterance, using the same c-Gaussians and evaluated against language specific HMMs. The language apriori knowledge (usefulness of feature vector) has been considered for the improvement of recognition performance. continuous hidden Markov model (cHMM) is designed using the new feature. The languages in OGI database are used for the study and we have achieved good performance.

关键词： fuzzy c-means algorithm New feature set Language Identification MFcc HMM and Usefulness weighted measure

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Fire Detection with Video Using fuzzy c-means and Back-Propagation Neural Network

Fire Detection with Video Using Fuzzy c-Means and Back-Propa...

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8th International Symposium on Neural Networks

作者： Truong, Tung Xuan Kim, Jong-Myon Univ Ulsan Sch Comp Engn & Informat Technol Ulsan South Korea

ISBN: (纸本)9783642210891

In this paper, we propose an effective method that detects fire automatically. The proposed algorithm is composed of four stages. In the first stage, an approximate median method is used to detect moving regions. In the second stage, a fuzzy c-means (FcM) algorithm based on the color of fire is used to select candidate fire regions from these moving regions. In the third stage, a discrete wavelet transform (DWT) is used to derive the approximated and detailed wavelet coefficients of sub-image. In the fourth stage, using these wavelet coefficients, a back-propagation neural network (BPNN) is utilized to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and low false alarm rate.

关键词： fire detection color segmentation fuzzy c-means algorithm back-propagation neural network

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fuzzy-Possibilistic Product Partition: A Novel Robust Approach to c-means clustering

Fuzzy-Possibilistic Product Partition: A Novel Robust Approa...

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8th International conference on Modeling Decisions for Artificial Intelligence (MDAI 2011)

作者： Szilagyi, Laszlo Sapientia Hungarian Sci Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783642225888

One of the main challenges in the field of c-means clustering models is creating an algorithm that is both accurate and robust. In the absence of outlier data, the conventional probabilistic fuzzy c-means (FcM) algorithm, or the latest possibilistic-fuzzy mixture model (PFcM), provide highly accurate partitions. However, during the 30-year history of FcM, the researcher community of the field failed to produce an algorithm that is accurate and insensitive to outliers at the same time. This paper introduces a novel mixture clustering model built upon probabilistic and possibilistic fuzzy partitions, where the two components are connected to each other in a qualitatively different way than they were in earlier mixtures. The fuzzy-possibilistic product partition c-means ((FPcM)-c-3) clustering algorithm seems to fulfil the initial requirements, namely it successfully suppresses the effect of outliers situated at any finite distance and provides partitions of high quality.

关键词： fuzzy c-means algorithm probabilistic partition possibilistic partition robust clustering

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SUPERPIXEL-BASED NEARSHORE SHIP DETEcTION METHOD ENHANcED BY fuzzy c-means cLUSTERING

SUPERPIXEL-BASED NEARSHORE SHIP DETECTION METHOD ENHANCED BY...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Hu, Wenlong Long, Jie Liu, Qian Liu, chang Wang, Qingsong Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen Peoples R China Beijing Inst Control & Elect Technol Beijing Peoples R China

ISBN: (纸本)9798350360332;9798350360325

Nearshore ship detection remains a challenging task due to the similarity in grayscale between harbor areas and ships. In this paper, we propose a new superpixel-based nearshore ship detection method, which effectively reduces land false alarms by the fuzzy c-means (FcM) algorithm. First, the method applies the simple linear iterative clustering (SLIc) algorithm to generate superpixel regions. Superpixels can preserve the boundary of the target and reduce the effects of speckle noise for target detection in synthetic aperture radar (SAR) images. Subsequently, we used the FcM algorithm to quantify the statistical differences between different superpixels, and the clustering results can serve as an indicative measure of the probability that each superpixel belongs to the sea category. Finally, we incorporate this probability into our proposed saliency detection method, facilitating the efficient identification of the ship regions. The experimental results show that the method can robustly and efficiently detect nearshore ship targets.

关键词： SAR image nearshore ship detection fuzzy c-means algorithm saliency detection.

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Differentially Private fuzzy c-means clustering algorithms for fuzzy Datasets 6

Differentially Private Fuzzy C-Means Clustering Algorithms f...

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6th Iranian Joint congress on fuzzy and Intelligent Systems (cFIS)

作者： Shakiba, Ali Vali E Asr Univ Rafsanjan Dept Comp Sci Rafsanjan Iran

ISBN: (纸本)9781538628362

In this paper, we propose two fuzzy clustering algorithms in the differential privacy scheme based on the fuzzy c-means algorithm. Up to the author's knowledge, these are the first algorithms of their kind which provide privacy for fuzzy data. Moreover, these two algorithms are experimentally compared with the original fuzzy algorithm and shown to be good approximation for the original fuzzy c-means algorithm.

关键词： fuzzy clustering Differential Privacy fuzzy c-means algorithm

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