Mineral emittance spectra: Clustering and classification using self-organizing maps

Spiking Self-organizing Maps for Classification Problem

RETURN TO ISSUEPREVNewsNEXTASU Drug Research Group DismantledELIZABETH WILSONCite this: Chem. Eng. News 2006, 84, 6, 10Publication Date (Print):February 6, 2006Publication History Published online14 November 2010Published inissue 6 February 2006https://pubs.acs.org/doi/10.1021/cen-v084n006.p010ahttps://doi.org/10.1021/cen-v084n006.p010anewsACS PublicationsCopyright © 2006 American Chemical SocietyArticle Views8Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access options SUBJECTS:Cancer,Pharmaceuticals,Students Get e-Alerts

QuestionCan a Kohonen's self‐organizing map, which is robust to non‐linear relationships between variables and their non‐normal distributions, be effective in patterning the data on plant communities investigated with the classical Braun‐Blanquet phytosociological method based on the ordinal scale? Does the application of the self‐organizing map make it possible to obtain new information from the analysed plant communities when compared to the Braun–Blanquet method alone?LocationThe Babsk nature reserve, Central Poland.MethodsThe analysed data were derived from two separate series of phytosociological studies on plant communities dating from periods 31 yr apart (1960 and 1991). The data consisted of 24 quantitative sampling lists of plant species (=phytosociological releves). The releves were analysed by the application of a Kohonen's self‐organizing map and the indicator value (IndVal).ResultsThe transformations from Querco roboris–Pinetum to Tilio cordatae–Carpinetum betuli in the vegetation in the Babsk nature reserve over 31 yr were determined precisely. Valuable new data were also obtained on: (1) significant associations of individual plant species with the previous and recent phytocoenoses, i.e. diagnostic groups of plant species for each phytocoenosis; (2) abiotic conditions (determined retrospectively in the two study periods on the basis of ecological indicators for vascular plants); and (3) the concordance of phytocoenoses with the biotope.ConclusionsThe Kohonen's self‐organizing map method and the IndVal made it possible to efficiently identify abiotic and biotic patterns for plant communities on the basis of the data expressed in the conventional Braun‐Blanquet scale.

Objects and processes classification is a common experimental problem. Its solution, first of all, is needed in automatic diagnosis systems, for example, to determine the equipment operation state through the diagnostic signal or to identify abnormalities with medical images. With the development of convolutional neural networks, new prospects for solving such problems have risen up. However, the classification accuracy that can be achieved on these networks is not sufficient enough for all diagnosis issues. It is subject to, for example, timely diagnosis of the onset of transient phenomena. At the same time, another type of neural network, Kohonen self-organizing maps, has a conceptual property for training on unclassified set of classes, that is, giving the opportunity to solve such an issue. Therefore, the accuracy enhancement of the classification on the basis of Kohonen networks implementation in the architecture of convolutional networks is a relevant objective and has practical significance. The article analyzes the means of improving the accuracy of convolutional neural networks and arising problems solution. The ways of increasing the proportion of correct clustering on Kohonen networks due to the ‘growth’ of its grid shape in determining new classes in the learning process are also given. It is this property that makes it possible to recognize transient phenomena. It is determined that the existing solutions of the combination of Kohonen networks and convolutional networks are aimed at improving the efficiency of only self-organizing maps, with the purpose to improve the accuracy of classification by convolutional networks, it became necessary to develop a new architecture. The paper provides a description of this issue. Since the initial information of the Kohonen networks is the weighting matrix values of the grid shape neurons, it was necessary to associate it with the representation of the images in order to process the diagnostic images. The paper proposes the concept of a built-in associative array block based on Kohonen networks. According to the proposed method, a software implementation of a hybrid neural network is developed. The formulation and results of computational experiments are presented. The efficiency of the proposed method is experimentally proved. Keywords: convolutional neural networks, CNN, Kohonen self-organizing maps, SOM, signal classification, image classification.

With an onset of social network amount of data is increasing day by day. In order to analyze the data and extract useful information, the data mining technology can be used. Clustering is one of the popular method of data mining. Clustering can be used for visualizing and analyzing of data. We are discussing Kohonen SOM. We are using neural networks, as a data mining tool which provides statistical observation and layout from big data-sets. We determine how Self-Organizing Kohonen Maps, can be efficiently used for data mining purposes. The Self Organizing Map (SOM) unsupervised learning is an effective computational tool in data mining processes. Self-Organizing Maps (SOMs) used to visualize social network dataset. We used Self-Organizing Map for clustering and analyzing high-dimensional and complex social network datasets. This paper also visualizes SOM neighbor connection, SOM neighbor weight distance, SOM weight position. We perform self organizing map algorithm for social network dataset in matlab.

The objective of this work is to demonstrate how clustering techniques such as Kohonen Self-Organizing Maps (SOM) can dramatically reduce the amount of time necessary for optimizing the reconfigurable aperture. Herein, a microstrip line-fed rectangular patch antenna is targeted as the reconfigurable aperture of interest. The patch element is divided into smaller elements, which by themselves are too small to radiate efficiently, yet in combination become a viable radiating structure. A 3-D finite-difference time-domain (FDTD) full-wave analysis utilizing perfectly matched layers is performed to determine the broadband return loss of a reconfigured aperture representing the optimization goal of our SOM. Simulations were performed for a large subset of possible state combinations of the reconfigurable aperture in order to cluster in an unsupervised way the states of the reconfigured apertures using SOM subject to broadband return loss. The SOM is shown to be an efficient method for reconfiguring the aperture from one state to the next within its respective cluster for large populations of feasible reconfigurable states.

Kohonen's self-organizing map (SOM), which performs topology-preserving transformation from a high dimensional data vector space to a low-dimensional map space, provides a powerful tool for data analysis, classification and visualization in many application fields. Despite its power, SOM can only deal with vectorized data, although many expansions have been proposed for various data-type cases. This study aims to develop a novel generalization of SOM called modular network SOM (mnSOM), which enables users to deal with general data classes in a consistent manner. mnSOM has an array structure consisting of function modules that are trainable neural networks, e.g. multi-layer perceptrons (MLPs), instead of the vector units of the conventional SOM family. In the case of MLP-modules, mnSOM learns a group of systems or functions in terms of the input-output relationships, and at the same time, mnSOM generates a feature map that shows distances between the learned systems. Thus, mnSOM with MLP modules is an SOM in function space rather than in vector space. From this point of view, the conventional SOM of Kohonen's can be regarded as a special case of mnSOM, the modules consisting of fixed-value bias units. In this paper, mnSOM with MLP modules is described along with some application examples.

The CIB Working Commission W117, “Performance Measurement in Construction,” is one of the more innovative and productive research-based commissions in CIB. It focuses on the utilization of performance metrics in the delivery of construction services. The home for W117 is the Performance Based Studies Research Group (PBSRG) at Arizona State University (ASU) in Tempe, Arizona, where W117 and ASU-PBSRG hold their annual Best Value Conference. From its start in 2009, W117 was led by Prof. Dean Kashiwagi (ASU), and his group of innovators (Dr. Kenneth Sullivan, Sylvia Romero, John Savicky and Dr. Jacob Kashiwagi) and co-coordinator, Professor Charles Egbu, (Glasgow Caledonian University). In 2016, W117 was joined by Co-Coordinator Prof. Sicco Santema, (University of Technology, Delft, Netherlands) the visionary who led to the proliferation of the W117 technology in the Netherlands.
 W117 aims to change construction procurement and stakeholder organizations worldwide through the use of the information-based Best Value Approach (BVA). As such, it differs from most CIB Commissions that are more science driven, while W117 is more concept and impact driven. It has been one of the most successful CIB Commissions in bridging the gap between the construction industry practice and academic research. It has been prolific in publishing and running research tests with industry partners. W117 and PBSRG have published over 300 papers and generated licensed technology (47 licenses from AZTech, the licensing body of ASU for intellectual property rights). It is the most licensed technology from the most innovative university in the U.S. (as rated by U.S. News and World Report (2016).

Identification of lithofacies using Kohonen self-organizing maps

Use of a fuzzy granulation–degranulation criterion for assessing cluster validity

Regional frequency analysis using Growing Neural Gas network

This study presents the discovery of meaningful patterns (groups) from the obese samples of health and nutritional survey data by applying various clustering techniques. Due to the mixed nature of the data (qualitative and quantitative variables) in the data set, the best-suited clustering techniques with appropriate dissimilarity metrics were chosen to interpret the meaningful results. The relationships between obesity and the lifestyle affecting factors like demography, socio-economic status, physical activity, and dietary behavior were assessed using four cluster techniques namely Two-Step clustering, Partition Around Medoids (PAM), Agglomerative Hierarchical clustering and, Kohonen Self Organizing Maps (SOMs). The solutions generated by these techniques were analyzed and validated by the help of cluster validity (CV) indices and later on their associations were determined with the obesity classes to discover the pattern from the obese sample. Two-Step clustering and hierarchical clustering outperformed the other applied techniques in identifying the subgroups based on the underlying hidden patterns in the data. Based on the CV indices values and the association analysis (obesity factor with the cluster solutions), two subgroups were generated and profiles of these groups have been reported. The first group belonged to the middle-aged individuals who seem to take care of their lifestyle while the other group belonged to young-aged individuals who in contrast to the first group presented a careless lifestyle factor (i.e., physical activity and dietary behavior). The salient features of these subgroups have been reported and can be proposed for the betterment in the health care industry. The research helped in identifying the interesting subsets/groups within survey data demonstrating similar characteristics and health status (i.e., prevalence of obesity with respect to lifestyle factors like physical activity, dietary behavior etc.) which will help to suggest appropriate measures/steps to be taken by the concerned departments to counter them and prevent in the population.

Describes a series of experiments on using Kohonen self-organizing maps and hybrid systems for continuous speech recognition. Experiments with different nonlinear transformations on the signal before using a neural network has been done and results compared. The hybrid system developed by the authors combines self-organizing feature maps with dynamic time warping. The experiments suggest that the combination has better performance than either of the two methods applied individually. >

The purpose of this article is to investigate customer clustering based on big data of the consumer loyalty system. The object of the research is the retail chains that are the clients of the Maxbonus system. Self-organizing Kohonen maps, implemented by the selforgmap function of the Deep Learning Toolbox module of the Matlab system, were used as a clustering tool. As a result of clustering 990 customers, classes invariant to the partition were identified, and it was shown that their number stabilizes with an increase in the number of classes in the Kohonen self-organizing map. The average indicators of representatives of each class have been determined. This result indicates the efficiency of the approach for clustering customers of customer loyalty systems. The prospect of the work is to include in the number of input parameters clustering the volume of purchases by product categories. Also promising is the transition to clustering retail chains participating in the Maxbonus system.

It is objective and practical for an electric machinery and apparatus test to computerize its diagnosis process with fault location identification. It is possible to recognize a fault location from measured voltage and current information by using a Kohonen's self-organizing map (SOM) which only needs a learning process of relation between the locations and their voltage and current characteristics. Diagnosis methods for some types of apparatus are proposed by using the SOM. First a diagnosis method for a three-phase PWM inverter which may have a switch fault is proposed. Twelve types of switch fault patterns are analyzed for its DC and harmonic components which are used as input data for their diagnosis map. The method is also applied to a multi-level, for example, three-level inverter. Secondly a diagnosis method of a linear induction motor which may have a turn or a turn-to-turn fault is proposed. Diagonal impedance for all fault cases under considerations are used for the SOM learning process and they are checked for their fault detections. Finally a diagnosis method for an induction machine which may have a turn fault is proposed. An impedance, which is a small slip-dependent term and expresses the degree of motor asymmetry, is computed from two sets of applied input voltages and currents and used as SOM input data. A simple mathematical model of an induction motor with the reference frame transformation is investigated. The above application examples are all validated for its simulated results by developed Matlab programs.