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Academic literature on the topic 'Cluster analysis Pattern recognition systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Cluster analysis Pattern recognition systems"

1

NATH, RAJIV KUMAR. "FINGERPRINT RECOGNITION USING MULTIPLE CLASSIFIER SYSTEM." Fractals 15, no. 03 (September 2007): 273–78. http://dx.doi.org/10.1142/s0218348x07003605.

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In this paper, the human fingerprint, which is independent of rotation and scaling, is recognized. The multiple classification technique, based on wavelet and fractal analysis, is used. It is shown that systematic incorporation of decision from various classifiers leads to a better decision rather than simply fusing them. Multiple classifiers can serve as a means of enhancing the performance of pattern recognition problems. Multiple classifier system design involves the problem of classifier fusion. This paper deals with multi-classifier systems in which each classifier uses its own representation of the input pattern, based on features collected from multiple sources. The multiple feature sources considered here are multi-fractals, wavelets and fast Fourier transforms coefficients. A clustering algorithm is used to observe the efficacy of the feature sources. The multiple sources were graded according to their effectiveness of providing more non-overlapping clusters for different groups into which the samples are to be separated. This approach first considers the best source for the feature parameters. If this feature classifies the test sample into more than one cluster, then the feature next to the best is summoned to finish up the remaining part of the classification process. The continuation of this process along with the judicious selection of classifiers succeeds in identifying a single cluster for the test sample. The results obtained after the experiments on a set of fingerprint images shows that this novel technique can go a long way in avoiding ambiguity and thus limiting the need for use of soft-computing tools for making decisions. Our method provides a hard, concrete and accurate solution to pattern recognition problems employing multiple classifiers.

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Zimovets, V. I., S. V. Shamatrin, D. E. Olada, and N. I. Kalashnykova. "Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode." Journal of Engineering Sciences 7, no. 1 (2020): E20—E27. http://dx.doi.org/10.21272/jes.2020.7(1).e4.

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The primary direction of the increase of reliability of the automated control systems of complex electromechanical machines is the application of intelligent information technologies of the analysis of diagnostic information directly in the operating mode. Therefore, the creation of the basics of information synthesis of a functional diagnosis system (FDS) based on machine learning and pattern recognition is a topical task. In this case, the synthesized FDS must be adaptive to arbitrary initial conditions of the technological process and practically invariant to the multidimensionality of the space of diagnostic features, an alphabet of recognition classes, which characterize the possible technical states of the units and devices of the machine. Besides, an essential feature of FDS is the ability to retrain by increasing the power of the alphabet recognition classes. In the article, information synthesis of FDS is performed within the framework of information-extreme intellectual data analysis technology, which is based on maximizing the information capacity of the system in the process of machine learning. The idea of factor cluster analysis was realized by forming an additional training matrix of unclassified vectors of features of a new recognition class obtained during the operation of the FDS directly in the operating mode. The proposed algorithm allows performing factor cluster analysis in the case of structured feature vectors of several recognition classes. In this case, additional training matrices of the corresponding recognition classes are formed by the agglomerative method of cluster analysis using the k-means procedure. The proposed method of factor cluster analysis is implemented on the example of information synthesis of the FDS of a multi-core mine lifting machine. Keywords: information-extreme intelligent technology, a system of functional diagnostics, multichannel mine lifting machine, machine learning, factor cluster analysis.

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LBOV, G. S. "LOGICAL DECISION RULES FOR AUTOMATIC DISCOVERY OF KNOWLEDGE IN EXPERT SYSTEMS DATABASE." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 01 (March 1989): 135–45. http://dx.doi.org/10.1142/s0218001489000127.

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We consider the class of logical decision rules and its applications for the solution of various problems of multivariate statistical analysis: discriminant and regression analysis, and cluster analysis. Some useful properties of the statistical analysis methods using the class under consideration are shown. Particular attention is paid to the possibility of presenting statistical results (unlike all other methods) in a language close to a natural language of logical statements.

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Tang, Hongyan, Ying Li, Tong Jia, Xiaoyong Yuan, and Zhonghai Wu. "Analysis of Frequently Failing Tasks and Rescheduling Strategy in the Cloud System." International Journal of Distributed Systems and Technologies 9, no. 1 (January 2018): 16–38. http://dx.doi.org/10.4018/ijdst.2018010102.

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To better understand task failures in cloud computing systems, the authors analyze failure frequency of tasks based on Google cluster dataset, and find some frequently failing tasks that suffer from long-term failures and repeated rescheduling, which are called killer tasks as they can be a big concern of cloud systems. Hence there is a need to analyze killer tasks thoroughly and recognize them precisely. In this article, the authors first investigate resource usage pattern of killer tasks and analyze rescheduling strategies of killer tasks in Google cluster to find that repeated rescheduling causes large amount of resource wasting. Based on the above observations, they then propose an online killer task recognition service to recognize killer tasks at the very early stage of their occurrence so as to avoid unnecessary resource wasting. The experiment results show that the proposed service performs a 93.6% accuracy in recognizing killer tasks with an 87% timing advance and 86.6% resource saving for the cloud system averagely.

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Kano, Makoto, Kunihiro Nishimura, Shuichi Tsutsumi, Hiroyuki Aburatani, Koichi Hirota, and Michitaka Hirose. "Cluster Overlap Distribution Map: Visualization for Gene Expression Analysis Using Immersive Projection Technology." Presence: Teleoperators and Virtual Environments 12, no. 1 (February 2003): 96–109. http://dx.doi.org/10.1162/105474603763835369.

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In this paper, we discuss possible applications of virtual reality technologies, such as immersive projection technology (IPT), in the field of genome science, and propose cluster-oriented visualization that attaches importance to data separation of large gene data sets with multiple variables. Based on these strategies, we developed the cluster overlap distribution map (CDCM), which is a visualization methodology using IPT for pairwise comparison between cluster sets generated from different gene expression data sets. This methodology effectively provides the user with indications of gene clusters that are worth a close examination. In addition, by using the plate window manager system, which enables the user to manipulate existing 2D GUI applications in the virtual 3D space, we developed the virtual environment for the comprehensive analysis from providing the indications to further examination by referring to the database on Web sites. Our system was applied in the comparison between the gene expression data sets of hepatocellular carcinomas and hepatoblastomas, and the effectiveness of the system was confirmed.

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Huang, Mingxia, Xuebo Yan, Zhu Bai, Haiqiang Zhang, and Zeen Xu. "Key Technologies of Intelligent Transportation Based on Image Recognition and Optimization Control." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (January 9, 2020): 2054024. http://dx.doi.org/10.1142/s0218001420540245.

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With the development of digital image processing technology, the application scope of image recognition is more and more wide, involving all aspects of life. In particular, the rapid development of urbanization and the popularization and application of automobiles in recent years have led to a sharp increase in traffic problems in various countries, resulting in intelligent transportation technology based on image processing optimization control becoming an important research field of intelligent systems. Aiming at the application demand analysis of intelligent transportation system, this paper designs a set of high-definition bayonet systems for intelligent transportation. It combines data mining technology and distributed parallel Hadoop technology to design the architecture and analysis of intelligent traffic operation state data analysis. The mining algorithm suitable for the system proves the feasibility of the intelligent traffic operation state data analysis system with the actual traffic big data experiment, and aims to provide decision-making opinions for the traffic state. Using the deployed Hadoop server cluster and the AdaBoost algorithm of the improved MapReduce programming model, the example runs large traffic data, performs traffic analysis and speed–overspeed analysis, and extracts information conducive to traffic control. It proves the feasibility and effectiveness of using Hadoop platform to mine massive traffic information.

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Wanner, Franz, Wolfgang Jentner, Tobias Schreck, Andreas Stoffel, Lyubka Sharalieva, and Daniel A. Keim. "Integrated visual analysis of patterns in time series and text data - Workflow and application to financial data analysis." Information Visualization 15, no. 1 (April 1, 2015): 75–90. http://dx.doi.org/10.1177/1473871615576925.

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In this article, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which are significantly connected in time to quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a priori method. First, based on heuristics, we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a priori method supports the discovery of such sequential temporal patterns. Then, various text features such as the degree of sentence nesting, noun phrase complexity, and the vocabulary richness, are extracted from the news items to obtain meta-patterns. Meta-patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time, cluster, and sequence visualization and analysis functionality. We provide a case study and an evaluation on financial data where we identify important future work. The workflow could be generalized to other application domains such as data analysis of smart grids, cyber physical systems, or the security of critical infrastructure, where the data consist of a combination of quantitative and textual time series data.

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BOIKO, O. V., V. V. TOMAREVA-PATLAHOVA, IU А. BONDAR, and M. S. KARPUNINA. "METHODICAL APPROACH TO ENSURING CLUSTER AND LOGISTICS DEVELOPMENT OF THE MARKET OF TRANSPORT SYSTEMS OF UKRAINE." Economic innovations 22, no. 4(77) (December 20, 2020): 29–38. http://dx.doi.org/10.31520/ei.2020.22.4(77).29-38.

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Topicality. In solving socio-economic problems in recent years, increasing contradictions between market participants at all levels of the national economy; This is especially true when it comes to the justification and practical implementation of possible options based on the analysis and further interpretation of empirical data relating to certain areas of development of territorial production systems or locally established industry markets, including transport services markets (RTP). within certain regions of the country. In this regard, there is a need to form a methodological framework for the possibility of implementing a cluster-logistics approach to the development of RTP. Aim and tasks. The purpose of writing this work is to develop a methodological support for cluster-logistics approach to the development of RTP with the definition of organizational forms of interaction of regional markets in the form of transport and logistics clusters (TLC). Research results. When analyzing and forecasting socio-economic phenomena, the researcher often encounters the multidimensionality of their description. Methods of multidimensional analysis are the most effective quantitative tool for the study of socio-economic processes, described by a large number of characteristics. These include cluster analysis, taxonomy, pattern recognition, factor analysis, and more. Cluster analysis most clearly reflects the features of multidimensional analysis in the classification, and factor analysis in the study of communication. The main purpose of cluster analysis is the breakdown of the set of studied objects and features into homogeneous groups in the appropriate sense of clusters. In cluster analysis, the concept of metrics is introduced to quantify similarity, and the similarity or difference between classified objects is set depending on the metric distance between them.In this paper, a single connection within a group of algorithms using a quadratic Euclidean distance is used. Cluster analysis most clearly reflects the features of multidimensional analysis in the classification. That is why a dimensionless model based on the use of relative coefficients of hierarchical agglomerative type is proposed to analyze the development of RTP and its transport infrastructure. The use of multidimensional classification methods allowed to group districts, in contrast to the traditional geographical or administrative division, by level of socio-economic development, which determines the needs of districts in transport infrastructure and cooperation, which, in particular through TLC, will ensure maximum use of existing economic potential. economy and equalization of living conditions of the population in different territories.Conclusion. Thus, the transport and socio-economic potential of the regions was analyzed, as a result of which two formed TLCs and five nuclei were identified, on the basis of which it is proposed to develop TLCs of appropriate types by joining regions with medium and low transport potential.

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YANG, MING-DER, TUNG-CHING SU, NANG-FEI PAN, and PEI LIU. "FEATURE EXTRACTION OF SEWER PIPE DEFECTS USING WAVELET TRANSFORM AND CO-OCCURRENCE MATRIX." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 02 (March 2011): 211–25. http://dx.doi.org/10.1142/s0219691311004055.

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In general, the sewer inspection usually employs a great number of CCTV images to discover sewer failures by human interpretation. A computer-aided program remains to be developed due to human's fatigue and subjectivity. To enhance the efficiency of sewer inspection, this paper attends to apply artificial intelligence to extract the failure features of the sewer systems that is demonstrated on the sewer system in the eastern Taichung City, Taiwan. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses, are adopted in this research to generate extracted features, which are the most valuable information in pattern recognition of failures on CCTV images. Wavelet transform is capable of dividing an image into four sub-images including approximation sub-image, horizontal detail sub-image, vertical detail sub-image, and diagonal detail sub-image. The co-occurrence matrices of horizontal orientation, vertical orientation, and 45° and 135° orientations, respectively, were calculated for the horizontal, vertical, and diagonal detail sub-images. Subsequently, the features including angular second moment, entropy, contrast, homogeneity, dissimilarity, correlation, and cluster tendency, can be obtained from the co-occurrence matrices. However, redundant features either decrease the accuracy of texture description or increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to search the appropriate feature sets according to the correlation coefficients between the features. In addition, a discriminant analysis was used to evaluate the discriminability of the features for the pipe failure defection, and entropy, correlation, and cluster tendency were found to be the best features based on the discriminant accuracy through an error matrix analysis.

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Chen, Min, and Simone A. Ludwig. "Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters." Journal of Artificial Intelligence and Soft Computing Research 4, no. 1 (January 1, 2014): 43–56. http://dx.doi.org/10.2478/jaiscr-2014-0024.

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Abstract Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used

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Dissertations / Theses on the topic "Cluster analysis Pattern recognition systems"

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Frigui, Hichem. "New approaches for robust clustering and for estimating the optimal number of clusters /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842528.

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Zhang, Lin. "PATTERN RECOGNITION METHODS FOR THE ANALYSIS OF INFRARED IMAGING DATA AND MULTIVARIATE CALIBRATION STANDARDIZATION FOR NEAR-INFARED SPECTROSCOPY." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1013445546.

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Nagaraja, Adarsh. "Feature pruning for action recognition in complex environment." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4992.

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A significant number of action recognition research efforts use spatio-temporal interest point detectors for feature extraction. Although the extracted features provide useful information for recognizing actions, a significant number of them contain irrelevant motion and background clutter. In many cases, the extracted features are included as is in the classification pipeline, and sophisticated noise removal techniques are subsequently used to alleviate their effect on classification. We introduce a new action database, created from the Weizmann database, that reveals a significant weakness in systems based on popular cuboid descriptors. Experiments show that introducing complex backgrounds, stationary or dynamic, into the video causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning the system or selecting better interest points. Instead, we show that the problem lies at the descriptor level and must be addressed by modifying descriptors.
ID: 030423225; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 40-41).
M.S.
Masters
Electrical Engineering and Computer Science
Engineering and Computer Science

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Hill, Evelyn June. "Applying statistical and syntactic pattern recognition techniques to the detection of fish in digital images." University of Western Australia. School of Mathematics and Statistics, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0070.

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This study is an attempt to simulate aspects of human visual perception by automating the detection of specific types of objects in digital images. The success of the methods attempted here was measured by how well results of experiments corresponded to what a typical human’s assessment of the data might be. The subject of the study was images of live fish taken underwater by digital video or digital still cameras. It is desirable to be able to automate the processing of such data for efficient stock assessment for fisheries management. In this study some well known statistical pattern classification techniques were tested and new syntactical/ structural pattern recognition techniques were developed. For testing of statistical pattern classification, the pixels belonging to fish were separated from the background pixels and the EM algorithm for Gaussian mixture models was used to locate clusters of pixels. The means and the covariance matrices for the components of the model were used to indicate the location, size and shape of the clusters. Because the number of components in the mixture is unknown, the EM algorithm has to be run a number of times with different numbers of components and then the best model chosen using a model selection criterion. The AIC (Akaike Information Criterion) and the MDL (Minimum Description Length) were tested.The MDL was found to estimate the numbers of clusters of pixels more accurately than the AIC, which tended to overestimate cluster numbers. In order to reduce problems caused by initialisation of the EM algorithm (i.e. starting positions of mixtures and number of mixtures), the Dynamic Cluster Finding algorithm (DCF) was developed (based on the Dog-Rabbit strategy). This algorithm can produce an estimate of the locations and numbers of clusters of pixels. The Dog-Rabbit strategy is based on early studies of learning behaviour in neurons. The main difference between Dog-Rabbit and DCF is that DCF is based on a toroidal topology which removes the tendency of cluster locators to migrate to the centre of mass of the data set and miss clusters near the edges of the image. In the second approach to the problem, data was extracted from the image using an edge detector. The edges from a reference object were compared with the edges from a new image to determine if the object occurred in the new image. In order to compare edges, the edge pixels were first assembled into curves using an UpWrite procedure; then the curves were smoothed by fitting parametric cubic polynomials. Finally the curves were converted to arrays of numbers which represented the signed curvature of the curves at regular intervals. Sets of curves from different images can be compared by comparing the arrays of signed curvature values, as well as the relative orientations and locations of the curves. Discrepancy values were calculated to indicate how well curves and sets of curves matched the reference object. The total length of all matched curves was used to indicate what fraction of the reference object was found in the new image. The curve matching procedure gave results which corresponded well with what a human being being might observe.

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Li, Na. "MMD and Ward criterion in a RKHS : application to Kernel based hierarchical agglomerative clustering." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0033/document.

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La classification non supervisée consiste à regrouper des objets afin de former des groupes homogènes au sens d’une mesure de similitude. C’est un outil utile pour explorer la structure d’un ensemble de données non étiquetées. Par ailleurs, les méthodes à noyau, introduites initialement dans le cadre supervisé, ont démontré leur intérêt par leur capacité à réaliser des traitements non linéaires des données en limitant la complexité algorithmique. En effet, elles permettent de transformer un problème non linéaire en un problème linéaire dans un espace de plus grande dimension. Dans ce travail, nous proposons un algorithme de classification hiérarchique ascendante utilisant le formalisme des méthodes à noyau. Nous avons tout d’abord recherché des mesures de similitude entre des distributions de probabilité aisément calculables à l’aide de noyaux. Parmi celles-ci, la maximum mean discrepancy a retenu notre attention. Afin de pallier les limites inhérentes à son usage, nous avons proposé une modification qui conduit au critère de Ward, bien connu en classification hiérarchique. Nous avons enfin proposé un algorithme itératif de clustering reposant sur la classification hiérarchique à noyau et permettant d’optimiser le noyau et de déterminer le nombre de classes en présence
Clustering, as a useful tool for unsupervised classification, is the task of grouping objects according to some measured or perceived characteristics of them and it has owned great success in exploring the hidden structure of unlabeled data sets. Kernel-based clustering algorithms have shown great prominence. They provide competitive performance compared with conventional methods owing to their ability of transforming nonlinear problem into linear ones in a higher dimensional feature space. In this work, we propose a Kernel-based Hierarchical Agglomerative Clustering algorithms (KHAC) using Ward’s criterion. Our method is induced by a recently arisen criterion called Maximum Mean Discrepancy (MMD). This criterion has firstly been proposed to measure difference between different distributions and can easily be embedded into a RKHS. Close relationships have been proved between MMD and Ward's criterion. In our KHAC method, selection of the kernel parameter and determination of the number of clusters have been studied, which provide satisfactory performance. Finally an iterative KHAC algorithm is proposed which aims at determining the optimal kernel parameter, giving a meaningful number of clusters and partitioning the data set automatically

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Zhu, Tao. "Extended cluster weighted modeling methods for transient recognition control." Diss., Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/zhu/ZhuT0806.pdf.

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Dannenberg, Matthew. "Pattern Recognition in High-Dimensional Data." Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/76.

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Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of Grassmann manifolds. This algorithm can consistently distinguish between a large range of even very similar materials, returning very accurate classification results with very little training data. When distinguishing between dissimilar locations like crop fields and forests, this algorithm consistently classifies more than 95 percent of points correctly. On more similar materials, more than 80 percent of points are classified correctly. This algorithm will allow for very accurate information to be extracted from these large and complicated hyperspectral images.

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Evans, Fiona H. "Syntactic models with applications in image analysis /." [Perth, W.A.] : [University of W.A.], 2006. http://theses.library.uwa.edu.au/adt-WU2007.0001.

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Dobie, Mark Ralph. "Motion analysis in multimedia systems." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359240.

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Chang, Charles Chung 1962. "Partial discharge pattern analysis." Monash University, Dept. of Electrical and Computer Systems Engineering, 2001. http://arrow.monash.edu.au/hdl/1959.1/8400.

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Books on the topic "Cluster analysis Pattern recognition systems"

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Structure in complex networks. Berlin: Springer, 2009.

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Bodade, Rajesh M. Iris analysis for biometric recognition systems. New York: Springer, 2014.

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Lawrence, Spitz A., Dengel Andreas, and International Association for Pattern Recognition., eds. International Association for Pattern Recognition Workshop on Document Analysis Systems. Singapore: World Scientific, 1995.

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Satchwell, Chris. Pattern recognition and trading decisions. New York: McGraw-Hill, 2005.

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Mumford, David. Pattern theory: The stochastic analysis of real-world signals. Natick, Mass: A K Peters, 2010.

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Mumford, David. Pattern theory: The stochastic analysis of real-world patterns. Natick, Mass: A K Peters, 2010.

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Agnès, Desolneux, ed. Pattern theory: The stochastic analysis of real-world patterns. Natick, Mass: A K Peters, 2010.

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Theodoridis, Sergios. Introduction to pattern recognition: A MATLAB approach. Burlington, MA: Academic Press, 2010.

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Image pattern recognition: Synthesis and analysis in biometrics. Singapore: World Scientific, 2007.

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Melin, Patricia. Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Book chapters on the topic "Cluster analysis Pattern recognition systems"

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Flasiński, Mariusz. "Pattern Recognition and Cluster Analysis." In Introduction to Artificial Intelligence, 141–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40022-8_10.

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Rzaḑca, Krzysztof, and Francesc J. Ferri. "Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm." In Pattern Recognition and Image Analysis, 868–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_100.

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Bubnicki, Zdzislaw. "Pattern Recognition." In Analysis and Decision Making in Uncertain Systems, 339–60. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3760-3_14.

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Kharin, Yurij. "Cluster Analysis under Distorted Model Assumptions." In Robustness in Statistical Pattern Recognition, 193–282. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-015-8630-6_7.

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Nicolau, Helena Bacelar. "On the Distribution Equivalence in Cluster Analysis." In Pattern Recognition Theory and Applications, 73–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_7.

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McLachlan, Geoffrey J., and David Peel. "Robust cluster analysis via mixtures of multivariate t-distributions." In Advances in Pattern Recognition, 658–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033290.

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Grass, P., and H. Fruhstorfer. "EEG Sleep Pattern Recognition by Cluster Analysis." In Medical Informatics Europe 85, 777. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-93295-3_151.

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Choi, Kang-Sun, and Ki-Won Oh. "Fast Simple Linear Iterative Clustering by Early Candidate Cluster Elimination." In Pattern Recognition and Image Analysis, 579–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_65.

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Du, Wei, and Justus Piater. "Tracking by Cluster Analysis of Feature Points and Multiple Particle Filters." In Pattern Recognition and Image Analysis, 701–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552499_77.

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Schierwagen, Andreas, Thomas Villmann, Alan Alpár, and Ulrich Gärtner. "Cluster Analysis of Cortical Pyramidal Neurons Using SOM." In Artificial Neural Networks in Pattern Recognition, 120–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12159-3_11.

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Conference papers on the topic "Cluster analysis Pattern recognition systems"

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Ma, Jin-xian, Shi-huai Xie, and Yong Chen. "Cluster Analysis for the Cognitive Selection of Nonlinear Programming Algorithms." In ASME 1990 Design Technical Conferences. American Society of Mechanical Engineers, 1990. http://dx.doi.org/10.1115/detc1990-0047.

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Abstract In recent years, cluster analysis has played an increasingly important role in statistical pattern recognition. Hoeltzel and Chieng have shown an example on cognitive selection of nonlinear programming algorithms in a mechanical design expert system. In this paper, an improved dynamic clustering of 3000 samples came from a comparative performance evaluation of six typical nonlinear programming softwares with randomly generated test problems has been made. Explanations resulting from the cluster analysis have been used to build rules to form the knowledge base of an optimization expert system.

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Catarino, A., A. Rocha, J. L. Monteiro, and F. Soares. "A Pattern Recognition System Based on Cluster and Discriminant Analysis for Fault Identification during Production." In 2007 IEEE International Symposium on Industrial Electronics. IEEE, 2007. http://dx.doi.org/10.1109/isie.2007.4374615.

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Makihara, Yasushi, and Yasushi Yagi. "Cluster-Pairwise Discriminant Analysis." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.146.

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4

Sinclair, D. "Cluster-based texture analysis." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.547191.

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Roberts, S. J. "Scale-space unsupervised cluster analysis." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546733.

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Momenan, Reza, Michael F. Insana, Robert F. Wagner, Brian S. Garra, and Murray H. Loew. "Application Of Cluster Analysis And Unsupervised Learning To Multivariate Tissue Characterization." In Pattern Recognition and Acoustical Imaging, edited by Leonard A. Ferrari. SPIE, 1987. http://dx.doi.org/10.1117/12.940261.

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Ou, Hui, John S. Allen, and Vassilis L. Syrmos. "Underwater Target Recognition Using Time-Frequency Analysis and Elliptical Fuzzy Clustering Classifications." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-80211.

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A novel underwater target recognition approach has been developed based on the use of Wigner-type Time-Frequency (TF) analysis and the elliptical Gustafson-Kessel (GK) clustering algorithm. This method is implemented for the acoustic backscattered signals of the targets, and more precisely from the examination of echo formation mechanisms in the TF plane. For each of the training signals, we generate a clustering distribution which represents the signal’s TF characteristics by a small number of clusters. A feature template is created by combining the clustering distributions for the signals from the same training target. In the classification process, we calculate the clustering distribution of the test signal and compare it with the feature templates. The target is discriminated in terms of the best match of the clustering pattern. The advantages of GK clustering are that it allows elliptical-shaped clusters, and it automatically adjusts their shapes according to the distribution of the TF feature patterns. The recognition scheme has been applied to discriminate four spherical shell targets filled with different fluids. The data sets are the simulated acoustic responses from these targets, including the interferences caused by the seafloor interaction. [J. A. Fawcett, W. L. J. Fox, and A. Maguer, J. Acoust. Soc. Am. 104, 3296–3304 (1998)]. To evaluate the system robustness, white Gaussian noise is added to the acoustic responses. More than 95% of correct classification is obtained for high Signal-to-Noise Ratio (SNR), and it is maintained around 70% for very low SNRs.

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He-Shan Guam and Qing-Shan Jiang. "Cluster financial time series for portfolio." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420788.

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"SYNC-SOM - Double-layer Oscillatory Network for Cluster Analysis." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004906703050309.

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Bo Tang, Yan-Dong Wang, and Ming-Tian Zhou. "Energy-Balanced Cluster Range Control algorithm for Wireless sensor networks." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420625.

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