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IEEE conference on computational Intelligence in Bioinformatics and computational Biology

作者： Yeesarapat, Uklid Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Kongpun, chatpat Chiang Mai Univ Fac Engn Dept Comp Engn Chiang Mai 50000 Thailand Chiang Mai Univ Fac Engn Ctr Biomed Engn Dept Comp Engn Chiang Mai Thailand Intercty Ctr Oral Hlth Dept Hlth Minist Publ Hlth Chiang Mai Thailand

ISBN: (纸本)9781479945351

Dental fluorosis occurs in many parts of the world because of highly exposure to high concentration of fluoride in the teeth development stage. To help the health policy makers developing the prevention and treatment plans, a manual or automatic image-based dental fluorosis classification system is needed. In this paper, we develop an automatic dental fluorosis classification system using multi-prototypes derived from the fuzzy c-means clustering algorithm. The values from red, green, blue, hue, saturation, and intensity channels are utilized as features in the algorithm. We also set the dental fluorosis classification criteria from the amount of pixels belonging to each class. We found that the pixel correct classification rate is around 92% on the training data set and around 90% on the blind test data set when comparing the results with two experts. Three out of seven images in the training data set and eight out of fifteen images in the blind test data set are correctly classified into dental fluorosis classes.

关键词： Dental fluorosis fuzzy c-means algorithm Multi-prototypes

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A Novel Approach for color Image Segmentation Using Iterative Partitioning Mean Shift clustering algorithm

A Novel Approach for Color Image Segmentation Using Iterativ...

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International conference on communications and Signal Processing

作者： P. Pedda Sadhu Naik T. Venu Gopal CSE Department JNTUK CSE Department JNTUHCE

ISBN: (纸本)9781479980826

Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields such as satellite, remote sensing, object identification, face tracking and most importantly medical applications. Here in this paper, we here supposed to propose a novel image segmentation using iterative partitioning mean shift clustering algorithm, which overcomes the drawbacks of conventional clustering algorithms and provides a good segmented images. Simulation performance shows that the proposed scheme has performed superior to the existing clustering methods.

关键词： Image segmentation H&E stain images fuzzy c-means algorithm Mean shift clustering

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copula-Based Pooled Frequency Analysis of Droughts in the canadian Prairies

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JOURNAL OF HYDROLOGIc ENGINEERING 2014年第2期19卷 277-289页

作者： Sadri, Sara Burn, Donald H. Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

This paper studies the frequency analysis of droughts using a copula with the application of regionalization in the context of a bivariate homogeneity analysis. Drought events indicated by severity and duration were extracted from monthly flow averages. A K-means clustering algorithm was used to form initial regions. A fuzzy c-means algorithm was used to form the final groups of sites that meet the criteria of bivariate discordancy, bivariate homogeneity, and size. The application of the Gumbel, clayton, and Frank copulas for bivariate drought frequency analysis was studied. Results show the importance of a clear definition of drought in every scenario since, in our example, the longest drought does not necessarily correspond to the most severe one. Another important observation of this study was that, given the average annual rainfall of a catchment, droughts seem to occur in almost all regions, humid or arid. However, areas with higher annual rainfall can experience shorter but more severe drought. The procedures of this study are applicable for flood frequency analysis as well. Furthermore, ungauged sites can be integrated in the procedure of regionalization.

关键词： Regionalization Regional drought frequency analysis fuzzy c-means algorithm Drought Bivariate discordancy Bivariate homogeneity L-comoments

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Detecting Temporal changes in customer Behavior

Detecting Temporal Changes in Customer Behavior

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International Electrical Engineering congress (iEEcON)

作者： Bose, Indranil chen, Xi Indian Inst Management Calcutta Kolkata India Zhejiang Univ Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781479931743

Extant research has studied customer behavior in a static manner. But customer clustering can be used to identify the dynamic behavioral patterns of customers over a period of time. We develop a method for extending the standard fuzzy c-means clustering algorithm for detection of temporal changes in customer data. The study using real-life data leads to detection of appearance of new clusters and disappearance of old clusters. Using cluster validity indexes the novel method is shown to lead to formation of clusters that are better than those produced by the fuzzy c-means (FcM) algorithm.

关键词： clusters fuzzy c-means algorithm Revenue Temporal data Usage Validity index

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clustering algorithm Based on fuzzy c-means and Artificial Fish Swarm

Clustering Algorithm Based on Fuzzy C-means and Artificial F...

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International Workshop on Information and Electronics Engineering (IWIEE) / International conference on Information, computing and Telecommunications (IcIcT)

作者： Zhu, Weiling Jiang, Jingqing Song, chuyi Bao, Lanying Inner Mongolia Univ Nationalities Coll Math Tongliao 028000 Peoples R China

clustering algorithm has applied in many fields such as data mining, statistics and machine learning. But the clustering number and the initial clustering center affect the accuracy of clustering. In this paper, the average information entropy and density function are used to determine the clustering number and the initial clustering center respectively based on fuzzy c-means clustering algorithm. And then the new bionic optimization algorithm---artificial fish swarm is applied to cluster. Artificial fish swarm algorithm is simple and easy to implement. The experimental results show the efficiency of the proposed clustering algorithm. (c) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

关键词： Artificial fish swarm algorithm Information entropy fuzzy c-means algorithm

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The fuzzy clustering combined an improved artificial bee colony algorithm with new rank fitness selection

The fuzzy clustering combined an improved artificial bee col...

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International conference on Software Intelligence Technologies and Applications & International conference on Frontiers of Internet of Things 2014

作者： He Yunbin Xiao Yupeng Wan Jing Li Song School of Computer Science and Technology Harbin University of Science and Technology China

The traditional fuzzy c-means clustering algorithm is easy to trap in local optimums as its sensitive selection of the initial cluster centers. For overcoming this disadvantage, this paper presents a fuzzy c-means algorithm combined an improved artificial bee colony algorithm with the strategy of rank fitness selection. The strategy is aimed to increase the selection probability of the individual with better fitness. The proposed algorithm combines the advantages of the high efficiency of fuzzy c-means algorithm and the global search ability of the artificial bee colony algorithm. The experiment and analysis results demonstrate that the algorithm can solve the optimization problem of the initial cluster centers with high robustness and better quality of clustering.

关键词： artificial bee colony algorithm clustering analysis fuzzy c-means algorithm artificial bee colony algorithm fuzzy health cluster Analysis algorithms Rank of data

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cross-evaluation of facial hyperpigmented lesions based on fluorescence color image and cross-polarized color image

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SKIN RESEARcH AND TEcHNOLOGY 2013年第1期19卷 E459-E464页

作者： Kim, Eunji Kim, Dongyoun choi, Eung H. Jung, Byungjo Yonsei Univ Dept Biomed Engn Wonju South Korea Yonsei Univ Inst Med Engn Wonju South Korea Yonsei Univ Dept Dermatol Wonju Coll Med Wonju South Korea

Background/purpose: Hyperpigmentation is a common skin problem that looks darker than normal skin regions. Accurate evaluation of a hyperpigmented lesion (HPL) is of clinical importance because proper choice of treatment can be dependent on it. This study aimed to differentiate between epidermal and dermal HPLs. Methods: cross-polarized color images (cPcIs) and fluorescence color images (FcIs) were acquired from the same facial regions. contrast-limited adaptive histogram equalization (cLAHE) was employed to enhance the image contrast and a fuzzy c-means algorithm was implemented to extract the HPLs. The HPLs were superimposed to investigate the difference between cPcI and FcI. Results: The HPL was successfully extracted by applying both cLAHE and fuzzy c-means algorithms. cPcI and FcI resulted in a slightly different HPL, even from the same facial region, indicating a greater percentage area of HPL in FcI than cPcI. conclusion: cPcI and FcI may be utilized to differentiate HPLs that exist in different skin layers. Thus, this approach may contribute to the effective treatment of HPLs.

关键词： hyper-pigmentation cross-polarization fluorescence cLAHE fuzzy c-means algorithm

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An efficient approach for unsupervised fuzzy clustering based on grouping evolution strategies

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PATTERN REcOGNITION 2013年第5期46卷 1240-1254页

作者： Kashan, Ali Husseinzadeh Rezaee, Babak Karimiyan, Somayyeh Islamic Azad Univ Fac Engn Dept Syst & Ind Engn Sci & Res Branch Tehran Iran Bojnord Univ Dept Ind Engn Bojnord Iran Int Inst Earthquake Engn & Seismol Dept Earthquake Engn Tehran Iran

fuzzy data clustering plays an important role in practical use and has become a prerequisite step for decision-making in fuzzy environment. In this paper we propose a new algorithm, called fuzzyGES for unsupervised fuzzy clustering based on adaptation of the recently proposed Grouping Evolution Strategy (GES). To adapt GES for fuzzy clustering we devise a fuzzy counterpart of the grouping mutation operator typically used in GES, and employ it in a two phase procedure to generate a new clustering solution. Unlike conventional clustering algorithms which should get the number of clusters as an input, fuzzyGES tries to determine the true number of clusters as well as providing the optimal cluster centroids after several iterations. The proposed approach is validated through using several data sets and results are compared with those of fuzzy c-means algorithm, particle swarm optimization algorithm (PSO), differential evolution (DE) and league championship algorithm (LcA). We also investigate the performance of fuzzyGES through using different cluster validity indices. Our results indicate that fuzzyGES is fast and provides acceptable results in terms of both determining the correct number of clusters and the accurate cluster centroids. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： clustering fuzzy Grouping evolution strategy fuzzy c-means algorithm

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URINE cOLOR AUTOMATIc IDENTIFIcATION DEVIcE BASED ON MIcROcONTROLLER FRAMEWORK AND cOLOR QUANTIZATION algorithm

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BIOMEDIcAL ENGINEERING-APPLIcATIONS BASIS cOMMUNIcATIONS 2014年第2期26卷 np页

作者： Lu, chuan-Pin chuang, Ming-Hui Li, Jui-Pin Yeh, Ta-Hua Meiho Univ Dept Informat Technol 23 Pingguang Rd Pingtung Taiwan

Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among patients receiving treatment in medical institutions. Such infections can be detected from urine color, like in the case of the purple urine bag syndrome. However, it is a difficult task for non-nursing care star and even the nursing staff to correctly conduct naked-eye identification without proper tools. To better assist both nursing and non-nursing care staff with the detection of infection signs in urine bag patients, a urine color automatic identification device has been developed. The device is based on microcontroller framework and color quantization algorithm. A hybrid color quantization algorithm and two features were proposed to identify the urine color. The identified color, as query data instead of human-described color keyword, can be used to retrieve the information from the database and then find possible symptoms for early warning. Instead of the nursing sta r, the device can automatically identify the patient's urine color. From experimental results, the device with the proposed algorithm shows its capability and feasibility of the urine color automatic identification.

关键词： Urine color identification fuzzy c-means algorithm Median cut algorithm Assistive technology devices

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clustering algorithm Based on fuzzy c-means and Artificial Fish Swarm

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Procedia Engineering 2012年 29卷 3307-3311页

作者： Weiling Zhu Jingqing Jiang chuyi Song Lanying Bao College of Mathematics Inner Mongolia University for NationalitiesTongliao 028000 China College of Computer Science and Technology Inner Mongolia University for NationalitiesTongliao 028000 China

clustering algorithm has applied in many fields such as data mining, statistics and machine learning. But the clustering number and the initial clustering center affect the accuracy of clustering. In this paper, the average information entropy and density function are used to determine the clustering number and the initial clustering center respectively based on fuzzy c-means clustering algorithm. And then the new bionic optimization algorithm----artificial fish swarm is applied to cluster. Artificial fish swarm algorithm is simple and easy to implement. The experimental results show the efficiency of the proposed clustering algorithm.

关键词： Artificial fish swarm algorithm Information entropy fuzzy c-means algorithm

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