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Methods for robust clustering of epileptic EEG spikes

IEEE TRANSAcTIONS ON BIOMEDIcAL ENGINEERING 2000年第7期47卷 857-868页

作者： Wahlberg, P Lantz, G Univ Lund Dept Elect Engn & Comp Sci Signal Proc Grp S-22100 Lund Sweden Univ Hosp HUG Dept Neurol Human Brain Mapping Lab CH-1211 Geneva 14 Switzerland

We investigate algorithms for clustering of epileptic electroencephalogram (EEG) spikes. Such a method is useful prior to averaging and inverse computations since the spikes of a patient often belong to a few distinct classes. Data sets often contain outliers, which makes algorithms with robust performance desirable. We compare the fuzzy c-means (FcM) algorithm and a graph-theoretic algorithm. We give criteria for determination of the correct level of outlier contamination. The performance is then studied by aid of simulations, which show good results for a range of circumstances, for both algorithms. The graph-theoretic method gave better results than FcM for simulated signals. Also, when evaluating the methods on seven real-life data sets, the graph-theoretic method was the better method, in terms of closeness to the manual assessment by a neurophysiologist. However, there was some discrepancy between manual and automatic clustering and we suggest as an alternative method a human choice among a limited set of automatically obtained clusterings. Furthermore, we evaluate geometrically weighted feature extraction and conclude that it is useful as a supplementary dimension for clustering.

关键词： clustering electrode geometry epileptic EEG spikes fuzzy c-means algorithm graph theory k-components localization of foci outlier contamination robustness

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A stratified sampling based clustering algorithm for large-scale data

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KNOWLEDGE-BASED SYSTEMS 2019年 163卷 416-428页

作者： Zhao, Xingwang Liang, Jiye Dang, chuangyin Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat Minist Educ Taiyuan 030006 Shanxi Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

Large-scale data analysis is a challenging and relevant task for present-day research and industry. As a promising data analysis tool, clustering is becoming more important in the era of big data. In large-scale data clustering, sampling is an efficient and most widely used approximation technique. Recently, several sampling-based clustering algorithms have attracted considerable attention in large-scale data analysis owing to their efficiency. However, some of these existing algorithms have low clustering accuracy, whereas others have high computational complexity. To overcome these deficiencies, a stratified sampling based clustering algorithm for large-scale data is proposed in this paper. Its basic steps include: (1) obtaining a number of representative samples from different strata with a stratified sampling scheme, which are formed by locality sensitive hashing technique, (2) partitioning the chosen samples into different clusters using the fuzzy c-means clustering algorithm, (3) assigning the out-of-sample objects into their closest clusters via data labeling technique. The performance of the proposed algorithm is compared with the state-of-the-art sampling-based fuzzy c-means clustering algorithms on several large-scale data sets including synthetic and real ones. The experimental results show that the proposed algorithm outperforms the related algorithms in terms of clustering quality and computational efficiency for large-scale data sets. (c) 2018 Published by Elsevier B.V.

关键词： Large-scale data fuzzy c-means algorithm Stratified sampling Data labeling

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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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Technique Based on fuzzy Logic for cotton Bale Lay-down Management

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FIBRES & TEXTILES IN EASTERN EUROPE 2017年第2期25卷 30-33页

作者： Das, Subhasis Ghosh, Anindya Govt Coll Engn & Text Technol Berhampur India

In this paper a new technique has been proposed for cotton bale management using fuzzy logic. The fuzzy c-means clustering algorithm has been applied for clustering cotton bales into 5 categories from 1200 randomly chosen bales of the J-34 variety. In order to cluster bales of different categories, eight fibre properties, viz., the strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered. The fuzzy c-means clustering method is able to handle the haziness that may be present in the boundaries between adjacent classes of cotton bales as compared to the K-means clustering method. This method may be used as a convenient tool for the consistent picking of different bale mixes from any number of bales in a warehouse.

关键词： cotton bale cluster analysis fibre property fuzzy logic fuzzy c-means algorithm

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Extended power-based aggregation of distance functions and application in image segmentation

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INFORMATION ScIENcES 2019年 494卷 155-173页

作者： Delic, Marija Nedovic, Ljubo Pap, Endre Univ Novi Sad Fac Tech Sci Dept Fundamentals Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Singidunum Univ Dept Postgrad Studies 32 Danijelova St Belgrade 11000 Serbia

In this paper, we psropose a novel method for construction of a distance function and demonstrate its application in image segmentation. In algorithms for image segmentation, distance functions represent a criterion which divides pixels into groups of segments. We introduce two extended aggregation functions, extended powers product and extended weighted arithmetic mean of powers. Their relevant properties are examined, as well as certain resulting properties of distance functions, which are constructed by an application of mentioned aggregation functions. In addition, one pixel descriptor, which is motivated by Local Binary Pattern family of descriptors (LBPs), is introduced and discussed. In the experimental section, we present an application of the introduced extended aggregation functions and descriptor, by a construction of a new distance function, used in fuzzy c-means clustering algorithm (FcM) for image segmentation. (c) 2019 Elsevier Inc. All rights reserved.

关键词： Distance function Metrics Extended aggregation function Local binary pattern Image segmentation fuzzy c-means algorithm

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A fuzzy entropy-constrained vector quantizer design algorithm and its applications to image coding

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IEIcE TRANSAcTIONS ON FUNDAMENTALS OF ELEcTRONIcS cOMMUNIcATIONS AND cOMPUTER ScIENcES 1999年第6期E82A卷 1109-1116页

作者： Hwang, WJ Hong, SL Chung Yuan Christian Univ Dept Elect Engn Chungli 32023 Taiwan

In this paper, a novel variable-rate vector quantizer (VQ) design algorithm using fuzzy clustering technique is presented. The algorithm, termed fuzzy entropy-constrained VQ (FEcVQ) design algorithm, has a better rate-distortion performance than that of the usual entropy-constrained VQ (EcVQ) algorithm for variable-rate VQ design. When performing the fuzzy clustering, the FEcVQ algorithm considers both the usual squared-distance measure, and the length of channel index associated with each codeword so that the average rate of the VQ can be controlled. In addition, the membership function for achieving the optimal clustering for the design of FEcVQ are derived. Simulation results demonstrate that the FEcVQ can be an effective alternative for the design of variable-rate VQs.

关键词： fuzzy clustering vector quantization image compression fuzzy c-means algorithm image processing

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Module division method of complex products for responding to user's requirements

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ALEXANDRIA ENGINEERING JOURNAL 2023年 82卷 404-413页

作者： Hao, Juan Gao, Xinqin Liu, Yong Han, Zhoupeng Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol Shaanxi Modern Equipment Green Mfg Collaborat Inn Xian 710048 Peoples R China

With the gradual diversification of personalized usage scenarios, user requirements play a direct role in product design decisions. Due to the problem of fuzzy demand caused by user cognitive bias, traditional design methods usually focus on realizing product functions and cannot effectively match user requirements. Therefore, this paper proposes a complex product module division method for user requirements. The method constitutes of three tasks, requirement analysis of module division, design mapping of module division and scheme implementation of module division. Firstly, based on the progressive architecture from initial requirements to precise requirements, the effective user requirements are obtained through similarity recommendation. Secondly, according to the four types of knowledge of function, geometry, physics and design, the design structure matrix is constructed to complete the Requirement-Function-Structure mapping. The improved fuzzy c-means algorithm is used to solve the mapping model, and finally a module division scheme that meets the user requirements is obtained. Taking the chip removal machine as an example, the rationality and effectiveness of the method are verified. The results show that the product modules divided by this method can effectively meet the multiple user requirements.

关键词： User requirements Modular division Design Structure Matrix fuzzy c-means algorithm

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A DESIGN ScHEME FOR A HIERARcHIcAL fuzzy PATTERN-MATcHING cLASSIFIER AND ITS APPLIcATION TO THE TIRE TREAD PATTERN-REcOGNITION

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fuzzy SETS AND SYSTEMS 1994年第2-3期65卷 311-322页

作者： JUNG, SW BAE, SW PARK, GT KOREA UNIV KOSFADV CONTROL & INSTRUMENT RES CTRDEPT ELECT ENGNSEOUL 136701SOUTH KOREA

We propose a design scheme for a hierarchical fuzzy pattern matching classifier (HFPMc) and apply it to the tire tread pattern recognition problem. In this design scheme, a binary decision tree is constructed at first by using fuzzy c-means (FcM) algorithm. At each node, a representative subset of features which can split best the labelled data into two dissimilar groups is selected from all the available features on the base of cluster validity. The cluster validity is evaluated under the two criteria. The one is the polarization degree, and the other is whether all the samples of a class belong to the same cluster or not. Then, a hierarchical cluster structure for the HFPMc is reconstructed by combining the successive nodes formed by the same representative subset of features. As the hierarchical classifier, is used a fuzzy pattern matching classifier in which the designer's intuitive knowledges about the pattern recognition problem can be easily incorporated. At each subhierarchy, the reference fuzzy sets and prototypes for the HFPMc are defined based on the cluster centers of the corresponding subhierarchy. The proposed design scheme is applied to the design of a HFPMc for the tire tread pattern recognition. The design procedure including feature extraction is described in detail. Experimental results show the usefulness of the proposed design scheme.

关键词： PATTERN REcOGNITION HIERARcHIcAL cLASSIFIER DEcISION TREE fuzzy c-means algorithm fuzzy PATTERN MATcHING FEATURE SELEcTION

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Automatic circuit tuning via unsupervised learning paradigms

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JOURNAL OF cIRcUITS SYSTEMS AND cOMPUTERS 2006年第2期15卷 217-242页

作者： El-Gamal, M. A. Abdel-Malek, H. L. Sorour, M. A. Cairo Univ Fac Engn Dept Engn Phys & Math Giza 12211 Egypt

This work describes a novel technique for automating the post-fabrication circuit tuning process. A training set that characterizes the behaviour of the circuit under test is first constructed. The data in this set consists of input measurement vectors with no output attributes, and is clustered via unsupervised learning algorithm in order to explore its underlying structure and correlations. The generated clusters are labeled and utilized in circuit tuning by calculating the value(s) of the tuning parameter(s). Three prominent and fundamentally different unsupervised learning algorithms, namely, the self-organizing map, the Gaussian mixture model, and the fuzzy c-means algorithm are employed and their performance is compared. The experimental results demonstrate that the proposed technique provides a robust and efficient circuit tuning approach.

关键词： circuit tuning clustering algorithms self-organizing map Gaussian mixture model fuzzy c-means algorithm

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Observation of summertime upwelling off the eastern and northeastern coasts of Hainan Island, china

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OcEAN DYNAMIcS 2016年第3期66卷 387-399页

作者： Lin, Peigen Hu, Jianyu Zheng, Quanan Sun, Zhenyu Zhu, Jia Xiamen Univ Coll Ocean & Earth Sci State Key Lab Marine Environm Sci Xiamen 361102 Fujian Peoples R China Univ Maryland Dept Atmospher & Ocean Sci College Pk MD 20742 USA

Using cruise observations and reanalysis data, this study analyzes the effects of wind, freshwater, and turbulent mixing on the two upwellings: one is off the eastern coast of Hainan Island (HEU) and the other is off the northeastern coast of Hainan Island (HNEU). During the cruise in 2009, the HNEU occurred with southwesterly to southeasterly wind. The relative large values of turbulent kinetic energy dissipation rate and diffusivity estimated from the Thorpe scale indicate that the upwelling water is further uplifted to the surface by strong turbulent mixing in the HNEU region. But the HEU was not observed under the southeasterly wind. During the cruise in 2012, the HNEU disappeared in the upper layer with freshwater covered and southeasterly wind, while the apparent HEU only accompanied with southwesterly wind. To obtain the general characteristics, we define three types of upwelling patterns, i.e., the intensified HEU, the intensified HNEU, and both HEU and HNEU in one day, using the reanalysis data. The composites of sea surface temperature (SST), wind, and precipitate for each upwelling pattern identify that the HNEU is associated with the prevailing southeasterly wind and can be limited in the lower layer when it is covered by freshwater. But the HEU is mainly driven by southwesterly wind but is not remarkably affected by freshwater.

关键词： Upwelling off Hainan Island Wind Freshwater Turbulent mixing fuzzy c-means algorithm

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