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Journal articles on the topic 'Kohonen Self-Organizing Map (SOM)'

Author: Grafiati

Published: 16 June 2021

Last updated: 1 February 2022

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1

Farhadi, Mahdi. "A self organizing map (SOM) based electric load classification." Facta universitatis - series: Electronics and Energetics 31, no. 4 (2018): 571–83. http://dx.doi.org/10.2298/fuee1804571f.

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It is of vital importance to use proper training data to perform accurate shortterm load forecasting (STLF) based on artificial neural networks. The pattern of the loads which are used for the training of Kohonen Self Organizing Map (SOM) neural network in STLF models should be of the highest similarity with the pattern of the electric load of the forecasting day. In this paper, an electric load classifier model is proposed which relies on the pattern recognition capability of SOM. The performance of the proposed electric load classifier method is evaluated by Iran electric grid data. The prop

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Ariana, Anak Agung Gede Bagus, I. Ketut Gede Darma Putra, and Linawati Linawati. "Perbandingan Metode SOM/Kohonen dengan ART 2 pada Data Mining Perusahaan Retail." Majalah Ilmiah Teknologi Elektro 16, no. 2 (2017): 55. http://dx.doi.org/10.24843/mite.2017.v16i02p10.

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Abstract— This study investigates the performance of artificial neural network method on clustering method. Using UD. Fenny’s customer profile in year 2009 data set with the Recency, Frequency and Monetary model data. Clustering methods were compared in this study is between the Self Organizing Map and Adaptive Resonance Theory 2. The performance evaluation method validation is measured by the index cluster validation. Validation index clusters are used, among others, Davies-Bouldin index, index and index Dunn Silhouette. The test results show the method Self Organizing Map is better to proces

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Khotimah, Tutik, and Darsin Darsin. "CLUSTERING PERKEMBANGAN KASUS COVID-19 DI INDONESIA MENGGUNAKAN SELF ORGANIZING MAP." Jurnal Dialektika Informatika (Detika) 1, no. 1 (2020): 23–26. http://dx.doi.org/10.24176/detika.v1i1.5596.

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Tujuan dari penelitian ini adalah melakukan pengelompokan daerah-daerah di Indonesia berdasarkan perkembangan kasus Covid-19. Pada penelitian ini digunakan Jaringan Syaraf Tiruan Kohonen yang disebut juga Self Organizing Map (SOM). Data yang digunakan adalah data situasi terkini penyebaran Covid-19 di Indonesia per tanggal 19 September 2020. Data ini diperoleh dari Kementerian Kesehatan Republik Indonesia.

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Pasa, Leandro Antonio, José Alfredo F. Costa, and Marcial Guerra de Medeiros. "An ensemble algorithm for Kohonen self-organizing map with different sizes." Logic Journal of the IGPL 25, no. 6 (2017): 1020–33. http://dx.doi.org/10.1093/jigpal/jzx046.

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Abstract Data Clustering aims to discover groups within the data based on similarities, with a minimal, if any, knowledge of their structure. Variations in the results may occur due to many factors, including algorithm parameters, initialization and stopping criteria. The usage of different attributes or even different subsets of data usually lead to different results. Self-organizing maps (SOM) has been widely used for a variety of tasks regarding data analysis, including data visualization and clustering. A machine committee, or ensemble, is a set of neural networks working independently wit

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Suwirmayanti, Ni Luh Gede Pivin. "Penerapan Teknik Clustering Untuk Pengelompokkan Konsentrasi Mahasiswa Dengan Metode Self Organizing Map." Jurnal Ilmiah Intech : Information Technology Journal of UMUS 2, no. 01 (2020): 11–20. http://dx.doi.org/10.46772/intech.v2i01.182.

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Prodi sistem Komputer memiliki beberapa konsentrasi atau peminatan ketika mahasiswa menginjak semester pertengahan yaitu semester 5. Penentuan konsetrasi ini sangatlah riskan untuk mahasiswa, karena mahasiswa harus memilih sesuai dengan bakat yang ditunjang oleh nilai dari matakuliah pendukung konsetrasi tersebut. Standar dalam menentukan konsentrasi bagi mahasiswa dapat dipengaruhi oleh beberapa faktor, antara lain nilai akdemikyang ditunjukkan dengan nilai matakuliah mahasiswa serta IPK dari mahasiswa tersebut. Penelitian ini bertujuan mengimplementasikan algoritma Self Organizing Map (SOM)

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Setyanngsih, Fatma Agus. "IMPLEMENTASI METODE KOHONEN UNTUK PREDIKSI CURAH HUJAN (STUDI KASUS : KOTA PONTIANAK)." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 4, no. 2 (2017): 198. http://dx.doi.org/10.20527/klik.v4i2.105.

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<p><em>The prediction to determine the rainfall in Pontianak is much needed. One of them is using a neural network algorithm using SOM (Self Organizing Maping) with the data used in January 2010-2013. The purpose of this study was to determine the rainfall prediction in the city of Pontianak with parameters of air temperature, relative humidity, air pressure and wind speed. The results showed that the value of MSE is obtained when studying the data network prediction in January of 2010 until 2013 using the Neural Network-SOM learning process with the amount of 1 neuron and using 12

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Kapita, Syarifuddin, and Saiful Abdullah. "APLIKASI JARINGAN SYARAF TIRUAN KOHONEN SELF ORGANIZING MAP (K-SOM) PADA DATA MUTU SEKOLAH." JIKO (Jurnal Informatika dan Komputer) 3, no. 1 (2020): 56–61. http://dx.doi.org/10.33387/jiko.v3i1.1737.

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Kolasa, Marta, Rafał Długosz, Wojciech Jóźwicki, Jolanta Pauk, Aleksandra Świetlicka, and Pierre André Farine. "Analysis of Significant Prognostic Factors of Patients with Bladder Cancer Using Self-Organizing Maps." Solid State Phenomena 199 (March 2013): 223–28. http://dx.doi.org/10.4028/www.scientific.net/ssp.199.223.

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This study presents a new approach to determine significant prognostic factors for patients suffering from the bladder cancer. The analysis of medical data has been performed by the use of the Kohonen self-organizing map (SOM). The SOM allows visualizing and identifying the prognostic factors indicating which of them are significant. A database comprised of ninety patients has been used in this study. Seven predictors were investigated. The cluster analysis indicates that the significant prognostic factors for the bladder cancer are: histological grade (cG) and stage (cT). The obtained results

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Schreck, Tobias, Jürgen Bernard, Tatiana von Landesberger, and Jörn Kohlhammer. "Visual Cluster Analysis of Trajectory Data with Interactive Kohonen Maps." Information Visualization 8, no. 1 (2009): 14–29. http://dx.doi.org/10.1057/ivs.2008.29.

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Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application

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Galvan, Diego, Luciane Effting, Hágata Cremasco, and Carlos Adam Conte-Junior. "The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks." Medicina 57, no. 3 (2021): 235. http://dx.doi.org/10.3390/medicina57030235.

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Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country

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Schulz, Reiner, and James A. Reggia. "Temporally Asymmetric Learning Supports Sequence Processing in Multi-Winner Self-Organizing Maps." Neural Computation 16, no. 3 (2004): 535–61. http://dx.doi.org/10.1162/089976604772744901.

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We examine the extent to which modified Kohonen self-organizing maps (SOMs) can learn unique representations of temporal sequences while still supporting map formation. Two biologically inspired extensions are made to traditional SOMs: selection of multiple simultaneous rather than single “winners” and the use of local intramap connections that are trained according to a temporally asymmetric Hebbian learning rule. The extended SOM is then trained with variable-length temporal sequences that are composed of phoneme feature vectors, with each sequence corresponding to the phonetic transcription

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Kuremoto, Takashi, Takahito Komoto, Kunikazu Kobayashi, and Masanao Obayashi. "Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System." Journal of Robotics 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/307293.

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An improved self-organizing map (SOM), parameterless-growing-SOM (PL-G-SOM), is proposed in this paper. To overcome problems existed in traditional SOM (Kohonen, 1982), kinds of structure-growing-SOMs or parameter-adjusting-SOMs have been invented and usually separately. Here, we combine the idea of growing SOMs (Bauer and Villmann, 1997; Dittenbach et al. 2000) and a parameterless SOM (Berglund and Sitte, 2006) together to be a novel SOM named PL-G-SOM to realize additional learning, optimal neighborhood preservation, and automatic tuning of parameters. The improved SOM is applied to construc

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Oyana, Tonny J., Luke E. K. Achenie, and Joon Heo. "The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data." Computational and Mathematical Methods in Medicine 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/683265.

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The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen’s SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional t

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Kolasa, Marta, Rafał Długosz, and Krzysztof Bieliński. "Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor Level." International Journal of Electronics and Telecommunications 56, no. 4 (2010): 367–73. http://dx.doi.org/10.2478/v10177-010-0048-6.

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Programmable, Asynchronous, Triangular Neighborhood Function for Self-Organizing Maps Realized on Transistor LevelA new hardware implementation of the triangular neighborhood function (TNF) for ultra-low power, Kohonen self-organizing maps (SOM) realized in the CMOS 0.18μm technology is presented. Simulations carried out by means of the software model of the SOM show that even low signal resolution at the output of the TNF block of 3-6 bits (depending on input data set) does not lead to significant disturbance of the learning process of the neural network. On the other hand, the signal resolut

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Thurn, Nicholas, Mary Williams, and Michael Sigman. "Application of Self-Organizing Maps to the Analysis of Ignitable Liquid and Substrate Pyrolysis Samples." Separations 5, no. 4 (2018): 52. http://dx.doi.org/10.3390/separations5040052.

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Classification of un-weathered ignitable liquids is a problem that is currently addressed by visual pattern recognition under the guidelines of Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatography-Mass Spectrometry, ASTM E1618-14. This standard method does not separately address the identification of substrate pyrolysis patterns. This report details the use of a Kohonen self-organizing map coupled with extracted ion spectra to organize ignitable liquids and substrate pyrolysis samples on a two-dimensional map with groupings that correspo

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Moonlight, Lady Silk. "SISTEM PENGENALAN WAJAH BERBASIS JARINGAN SYARAF TIRUAN SELF ORGANIZINGMAP (SOM) DENGAN PEMROSESAN AWAL DISCRETE COSINE TRANSFORM (DCT)." Jurnal Penelitian 4, no. 3 (2019): 29–39. http://dx.doi.org/10.46491/jp.v4e3.372.29-39.

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Citra wajah merupakan salah satu fitur biometrik yang dapat dijadikan sebagai bukti autentik dari seseorang. Sistem pengenalan wajah (Face Recognition) secara komputerisasi, akan mengetahui identitas diri seseorang. Dalam proses pelatihan citra wajah, penggunaan piksel dari citra wajah secara langsung dapat mengakibatkan banyaknya fitur-fitur wajah yang tidak dapat terekstraksi dengan baik. Maka dari itu diperlukan suatu pemrosesan awal yang dapat mengekstraksi fitur-fitur wajah dengan baik. Dimana pada penelitian ini digunakan Discrete Cosine Transform (DCT) sebagai pemrosesan awal dan Penggu

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Costea, Adrian. "On building early-warning systems for preventing the deterioration of financial institutions’ performance." Proceedings of the International Conference on Applied Statistics 1, no. 1 (2019): 194–202. http://dx.doi.org/10.2478/icas-2019-0017.

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Abstract This paper assesses the financial performance of Romania’s non-banking financial institutions (NFIs) using a neural network training algorithm proposed by Kohonen, namely the Self-Organizing Maps algorithm. The algorithm takes the financial dataset and positiones each observation into a self-organizing map (a two-dimensional map) which can be latter used to visualize the trajectories of an individual NFI and explain it based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. Further, we use the map as an early-warning system that would ac

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de Matos, Marcílio Castro, Paulo Léo Osorio, and Paulo Roberto Johann. "Unsupervised seismic facies analysis using wavelet transform and self-organizing maps." GEOPHYSICS 72, no. 1 (2007): P9—P21. http://dx.doi.org/10.1190/1.2392789.

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Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps (SOMs) as a new way to build seismic facies maps and to estimate the number of seismic facies. In the sec

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Bondarenko, Andrey, and Arkady Borisov. "Research of Artificial Neural Networks Abilities in Printed Words Recognition." Scientific Journal of Riga Technical University. Computer Sciences 42, no. 1 (2010): 124–29. http://dx.doi.org/10.2478/v10143-010-0053-3.

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Research of Artificial Neural Networks Abilities in Printed Words Recognition This paper provides a brief overview on document analysis and recognition area, highlighting main steps and modules that are used to build recognition systems of the mentioned type. We underline basic workflow of such system down to the problem of single character recognition problem and highlighting possibilities and ways for artificial neural networks usage. Further we are conducting a formal comparison of abilities of printed characters recognition between two well known types of second generation neural networks,

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Barletta, Vita Santa, Danilo Caivano, Antonella Nannavecchia, and Michele Scalera. "Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach." Future Internet 12, no. 7 (2020): 119. http://dx.doi.org/10.3390/fi12070119.

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The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of it

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Deetz, Marcus. "K-Means Clustering of Self-Organizing Maps: An Empirical Study on the Information Content of Self-Classification of Hedge Fund Managers." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 5, no. 3 (2019): 43–57. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.53.1006.

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With the implementation of the 2-step approach according to Vesanto & Alhoniemi (2000), this article extends the procedure of visual evaluation of the Kohonen Maps usually chosen in the hedge fund literature for classification with Self-Organizing Maps. It introduces an automated procedure which guarantees a consistent combination of adjacent output units and thus an objective classification. The practical application of this method results in a reduction of the strategy groups specified by the database. This is also accompanied by a significant reduction in the Davies Bouldin Index (DBI)

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Bishop, Christopher M., Markus Svensén, and Christopher K. I. Williams. "GTM: The Generative Topographic Mapping." Neural Computation 10, no. 1 (1998): 215–34. http://dx.doi.org/10.1162/089976698300017953.

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Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis, which is based on a linear transformation between the latent space and the data space. In this article, we introduce a form of nonlinear latent variable model called the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization algorithm. GTM provides a principled alternative to the widely used self-organizing map (SOM) of Kohonen (19

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Barletta, Vita Santa, Danilo Caivano, Antonella Nannavecchia, and Michele Scalera. "A Kohonen SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks." Applied Sciences 10, no. 15 (2020): 5062. http://dx.doi.org/10.3390/app10155062.

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The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both

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Várbíró, Gábor, Gábor Borics, Keve T. Kiss, Katalin E. Szabó, Andelka Plenković-Moraj, and Éva Ács. "Use of Kohonen Self Organizing Maps (SOM) for the characterization of benthic diatom associations of the River Danube and its tributaries." River Systems 17, no. 3-4 (2007): 395–403. http://dx.doi.org/10.1127/lr/17/2007/395.

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Pham, D. T., M. S. Packianather, and E. Y. A. Charles. "Control chart pattern clustering using a new self-organizing spiking neural network." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 222, no. 10 (2008): 1201–11. http://dx.doi.org/10.1243/09544054jem1054.

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This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection delays instead of the previous approaches of delay selection. Here the tuned delays compensate the differences in the input firing times of temporal patterns and enables them to coincide. The coincidence detection capability of the spiking neuron has been utilized for pattern clustering. The structure of

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Ovechkin, Maksim V., Eugeniy S. Shelihov, and Julia I. Ovechkina. "The analysis of methods effectiveness of automated non-destructive testing of products based on Data Mining methods." MATEC Web of Conferences 224 (2018): 02062. http://dx.doi.org/10.1051/matecconf/201822402062.

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Purpose of the study: the analysis of the effectiveness of automated nondestructive testing methods within the objectives of data clustering on the use of short-wave electromagnetic radiation in flaw detection. Research methods: Kohonen self-organizing maps (SOM). The relevance of the work is that due to the increased demand for quality and reliability of products are becoming increasingly important physical methods for automated control of metals and products thereof that do not require cutting or fracture specimens of finished productes. The article noted common features of methods of short-

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Bonifácio Oliveira Cardoso, Daniel, Luiza Amaral Medeiros, Gabriela de Oliveira Carvalho, et al. "Use of computational intelligence in the genetic divergence of colored cotton plants." Bioscience Journal 37 (January 20, 2021): e37007. http://dx.doi.org/10.14393/bj-v37n0a2021-53634.

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The objective of this work was to analyze the genetic diversity using conventional methods and artificial neural networks among 12 colored fiber cotton genotypes, using technological characteristics of the fiber and productivity in terms of cottonseed and cotton fiber yield. The experiment was conducted in an experimental area located at Fazenda Capim Branco, belonging to the Federal University of Uberlândia, in the city of Uberlândia, Minas Gerais. Twelve genotypes of colored fiber cotton were evaluated, 10 from the Cotton Genetic Improvement Program (PROMALG): UFUJP - 01, UFUJP - 02, UFUJP -

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Toiviainen, Petri, and Carol L. Krumhansl. "Measuring and Modeling Real-Time Responses to Music: The Dynamics of Tonality Induction." Perception 32, no. 6 (2003): 741–66. http://dx.doi.org/10.1068/p3312.

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We examined a variety of real-time responses evoked by a single piece of music, the organ Duetto BWV 805 by J S Bach. The primary data came from a concurrent probe-tone method in which the probe-tone is sounded continuously with the music. Listeners judged how well the probe tone fit with the music at each point in time. The process was repeated for all probe tones of the chromatic scale. A self-organizing map (SOM) [Kohonen 1997 Self-organizing Maps (Berlin: Springer)] was used to represent the developing and changing sense of key reflected in these judgments. The SOM was trained on the probe

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En-Naimani, Zakariae, Mohamed Lazaar, and Mohamed Ettaouil. "Architecture Optimization Model for the Probabilistic Self-Organizing Maps and Speech Compression." International Journal of Computational Intelligence and Applications 15, no. 02 (2016): 1650007. http://dx.doi.org/10.1142/s1469026816500073.

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The probabilistic self-organizing map (PRSOM) is an improved version of the Kohonen classical model (SOM) that appeared in the late 1990’s. In the last years, the interest of probabilistic methods, especially in the fields of clustering and classification has increased, and the PRSOM has been successfully employed in many technological uses, such as: pattern recognition, speech recognition, data compression, medical diagnosis, etc. Mathematically, the PRSOM gives an estimation of the density probability function of a set of samples. And this estimation depends on the parameters given by the ar

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Thomas, Elizabeth, Marc M. Van Hulle, and Rufin Vogel. "Encoding of Categories by Noncategory-Specific Neurons in the Inferior Temporal Cortex." Journal of Cognitive Neuroscience 13, no. 2 (2001): 190–200. http://dx.doi.org/10.1162/089892901564252.

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In order to understand how the brain codes natural categories, e.g., trees and fish, recordings were made in the anterior part of the macaque inferior temporal (IT) cortex while the animal was performing a tree/nontree categorization task. Most single cells responded to exemplars of more than one category while other neurons responded only to a restricted set of exemplars of a given category. Since it is still not known which type of cells contribute and what is the nature of the code used for categorization in IT, we have performed an analysis on single-cell data. A Kohonen self-organizing ma

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Soldic-Aleksic, Jasna, and Rade Stankic. "A comparative analysis of Serbia and the EU member states in the context of networked readiness index values." Ekonomski anali 60, no. 206 (2015): 45–86. http://dx.doi.org/10.2298/eka1506045s.

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Nowadays it is generally accepted that information and communication technologies (ICT) are important drivers and ?enabling? technologies that have a broad impact on many sectors of the economy and social life. Therefore, measuring the level of ICT development, their economic and social impact, and the country?s readiness to use them are of great importance. In this paper we present the conceptual framework of the Networked Readiness Index (NRI) proposed by the World Economic Forum, and analyse the relative position of Serbia and its ?distance? from the EU member states in the domain of NRI in

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Tachibana, Kanta, and Takeshi Furuhashi. "Self-Organizing Map with Generating and Moving Neurons in Visible Space." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (2007): 626–32. http://dx.doi.org/10.20965/jaciii.2007.p0626.

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Kohonen’s Self-Organizing feature Map (SOM) is used to obtain topology-preserving mapping from high-dimensional feature space to visible space of two or fewer dimensions. The SOM algorithm uses a fixed structure of neurons in visible space and learns a dataset by updating reference points in feature space. The mapping result depends on mapping parameters fixed, which are the number and visible positions of neurons, and parameters of learning, which are the learning rate, total iteration, and the setting of neighboring radii. To obtain a satisfactory result, the user usually must try many combi

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Curry, Bruce, Fiona Davies, Martin Evans, Luiz Moutinho, and Paul Phillips. "The Kohonen Self-organising Map as an Alternative to Cluster Analysis: An Application to Direct Marketing." International Journal of Market Research 45, no. 2 (2003): 1–20. http://dx.doi.org/10.1177/147078530304500205.

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This paper examines the potential of the Kohonen self-organising map (SOM) in a marketing context. It deals specifically with consumer attitudes towards direct marketing. The SOM belongs to the general class of neural network (NN) models, but differs from the now orthodox way in which NNs are implemented. The major difference is that network learning is ‘unsupervised’, in which case the SOM is related to clustering methods. The result of an SOM is a two-dimensional grid of related ‘prototypes’ rather than non-overlapping clusters. The method involves iterative adjustment of the prototypes in s

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Haese, Karin, and Geoffrey J. Goodhill. "Auto-SOM: Recursive Parameter Estimation for Guidance of Self-Organizing Feature Maps." Neural Computation 13, no. 3 (2001): 595–619. http://dx.doi.org/10.1162/089976601300014475.

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An important technique for exploratory data analysis is to form a mapping from the high-dimensional data space to a low-dimensional representation space such that neighborhoods are preserved. A popular method for achieving this is Kohonen's self-organizing map (SOM) algorithm. However, in its original form, this requires the user to choose the values of several parameters heuristically to achieve good performance. Here we present the Auto-SOM, an algorithm that estimates the learning parameters during the training of SOMs automatically. The application of Auto-SOM provides the facility to avoi

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Hanumantha Rao, T. V. K., Saurabh Mishra, and Sudhir Kumar Singh. "Automatic Electrocardiographic Analysis Using Artificial Neural Network Models." Advanced Materials Research 403-408 (November 2011): 3587–93. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3587.

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In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features

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Urbański, Krzysztof, and Stanisław Gruszczyński. "Adaptive modelling of spatial diversification of soil classification units." Journal of Water and Land Development 30, no. 1 (2016): 127–39. http://dx.doi.org/10.1515/jwld-2016-0029.

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AbstractThe article presents the results of attempts to use adaptive algorithms for classification tasks different soils units. The area of study was the Upper Silesian Industrial Region, which physiographic and soils parameters in the form of digitized was used in the calculation. The study used algorithms, self-organizing map (SOM) of Kohonen, and classifiers: deep neural network, and two types of decision trees: Distributed Random Forest and Gradient Boosting Machine. Especially distributed algorithm Random Forest (algorithm DRF) showed a very high degree of generalization capabilities in m

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Tambouratzis, Tatiana, Dina Chernikova, and Imre Pzsit. "Pulse Shape Discrimination of Neutrons and Gamma Rays Using Kohonen Artificial Neural Networks." Journal of Artificial Intelligence and Soft Computing Research 3, no. 2 (2013): 77–88. http://dx.doi.org/10.2478/jaiscr-2014-0006.

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Abstract The potential of two Kohonen artificial neural networks I ANNs) - linear vector quantisa - tion (LVQ) and the self organising map (SOM) - is explored for pulse shape discrimination (PSD), i.e. for distinguishing between neutrons (n's) and gamma rays (γ’s). The effect that la) the energy level, and lb) the relative- of the training and lest sets, have on iden- tification accuracy is also evaluated on the given PSD datasel The two Kohonen ANNs demonstrate compfcmentary discrimination ability on the training and test sets: while the LVQ is consistently mote accurate on classifying the tr

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Alcan, Veysel, Hilal Kaya, Murat Zinnuroğlu, Gülçin Kaymak Karataş, and Mehmet Rahmi Canal. "A novel approach to the diagnostic assessment of carpal tunnel syndrome based on the frequency domain of the compound muscle action potential." Biomedical Engineering / Biomedizinische Technik 65, no. 1 (2020): 61–71. http://dx.doi.org/10.1515/bmt-2018-0077.

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AbstractConventional electrophysiological (EP) tests may yield ambiguous or false-negative results in some patients with signs and symptoms of carpal tunnel syndrome (CTS). Therefore, researchers tend to investigate new parameters to improve the sensitivity and specificity of EP tests. We aimed to investigate the mean and maximum power of the compound muscle action potential (CMAP) as a novel diagnostic parameter, by evaluating diagnosis and classification performance using the supervised Kohonen self-organizing map (SOM) network models. The CMAPs were analyzed using the fast Fourier transform

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Kosugi, Atsushi, Kok Hoong Leong, Eri Urata, et al. "Effect of Different Direct Compaction Grades of Mannitol on the Storage Stability of Tablet Properties Investigated Using a Kohonen Self-Organizing Map and Elastic Net Regression Model." Pharmaceutics 12, no. 9 (2020): 886. http://dx.doi.org/10.3390/pharmaceutics12090886.

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This study tested 15 direct compaction grades to identify the contribution of different grades of mannitol to the storage stability of the resulting tablets. After preparing the model tablets with different values of hardness, they were stored at 25 °C, 75% relative humidity for 1 week. Then, measurement of the tablet properties was conducted on both pre- and post-storage tablets. The tablet properties were tensile strength (TS), friability, and disintegration time (DT). The experimental data were analyzed using a Kohonen self-organizing map (SOM). The SOM analysis successfully classified the

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Ewert, Pawel, Teresa Orlowska-Kowalska, and Kamila Jankowska. "Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks." Energies 14, no. 3 (2021): 712. http://dx.doi.org/10.3390/en14030712.

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Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal

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Cheh, John J., Evgeny A. Lapshin, and Il-Woon Kim. "An Application of Self-Organizing Maps to Financial Structure Analysis of Keiretsu versus Non-Keiretsu Firms in Japan." Review of Pacific Basin Financial Markets and Policies 09, no. 03 (2006): 405–29. http://dx.doi.org/10.1142/s0219091506000781.

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It has been argued that keiretsu in Japan allows its member firms to maintain a financial structure different from that of non-keiretsu member firms. In this paper, we use two different types of financial statement ratio analysis techniques to discover whether Kohonen's self-organizing map (SOM) is able to uncover the differences in financial structures between keiretsu and non-keiretsu firms: ad hoc financial ratios and valuation-based financial ratios. We have found some evidence that SOM enables both financial analysis techniques to recognize different financial structures between the two g

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Koishi, M., and Z. Shida. "Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2." Tire Science and Technology 34, no. 3 (2006): 170–94. http://dx.doi.org/10.2346/1.2345640.

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Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionar

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Assal, Samy F. M. "Self-organizing approach for learning the forward kinematic multiple solutions of parallel manipulators." Robotica 30, no. 6 (2011): 951–61. http://dx.doi.org/10.1017/s0263574711001172.

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SUMMARYContrary to the inverse kinematics, the forward kinematics of parallel manipulators involves solving highly non-linear equations and provides more than one feasible end-effector pose, which are called the assembly modes, for a given set of link lengths or joint angles. Out of the multiple feasible solutions, only one solution can be achieved from a certain initial configuration. Therefore, in this paper, the Kohonen's self-organizing map (SOM) is proposed to learn and classify the multiple solution branches of the forward kinematics and then provide a unique real-time solution among the

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Mulier, Filip, and Vladimir Cherkassky. "Self-Organization as an Iterative Kernel Smoothing Process." Neural Computation 7, no. 6 (1995): 1165–77. http://dx.doi.org/10.1162/neco.1995.7.6.1165.

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Kohonen's self-organizing map, when described in a batch processing mode, can be interpreted as a statistical kernel smoothing problem. The batch SOM algorithm consists of two steps. First, the training data are partitioned according to the Voronoi regions of the map unit locations. Second, the units are updated by taking weighted centroids of the data falling into the Voronoi regions, with the weighing function given by the neighborhood. Then, the neighborhood width is decreased and steps 1, 2 are repeated. The second step can be interpreted as a statistical kernel smoothing problem where the

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Abarca-Alvarez, Campos-Sánchez, and Mora-Esteban. "Survey Assessment for Decision Support Using Self-Organizing Maps Profile Characterization with an Odds and Cluster Heat Map: Application to Children's Perception of Urban School Environments." Entropy 21, no. 9 (2019): 916. http://dx.doi.org/10.3390/e21090916.

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The interpretation of opinion and satisfaction surveys based exclusively on statistical analysis often faces difficulties due to the nature of the information and the requirements of the available statistical methods. These difficulties include the concurrence of categorical information with answers based on Likert scales with only a few levels, or the distancing of the necessary heuristic approach of the decision support system (DSS). The artificial neural network used for data analysis, called Kohonen or self-organizing maps (SOM), although rarely used for survey analysis, has been applied i

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Harchli, Fidae, Zakariae En-Naimani, Abdelatif Es-Safi, and Mohamed Ettaouil. "Vector Quantization for Speech Compression by a New Version of PRSOM." International Journal on Artificial Intelligence Tools 27, no. 03 (2018): 1850013. http://dx.doi.org/10.1142/s0218213018500136.

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The self-organizing map (SOM) is a popular neural network which was designed for solving problems that involve tasks such as clustering and visualization. Especially, it provides a new strategy of clustering using a competition and co-operation principal. The probabilistic Kohonen network (PRSOM) is the stochastic version of classical one. However, determination of the optimal number of neurons, their initial weights vector and their deviation matrix is still a big problem in the literature. These parameters have a great impact on the learning process of the network, the convergence and the qu

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Kauko, Tom. "Using the Self-Organising Map to Identify Regularities across Country-Specific Housing-Market Contexts." Environment and Planning B: Planning and Design 32, no. 1 (2005): 89–110. http://dx.doi.org/10.1068/b3186.

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The aim of exploring and monitoring housing-market fundamentals (prices, dwelling features, area density, residents, and so on) on a macrolocational level relates to both public and private sector policymaking. Housing market segmentation (that is, the emergence of housing submarkets), a concept with increasing relevance, is defined as the differentiation of housing in terms of the income and preferences of the residents and in terms of administrative circumstances. In order to capture such segmentation empirically, the author applies a fairly new and emerging technique known as the ‘self-orga

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Peeters, L., F. Bação, V. Lobo, and A. Dassargues. "Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map." Hydrology and Earth System Sciences Discussions 3, no. 4 (2006): 1487–516. http://dx.doi.org/10.5194/hessd-3-1487-2006.

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Abstract. The use of unsupervised artificial neural network techniques like the self-organizing map (SOM) algorithm has proven to be a useful tool in exploratory data analysis and clustering of multivariate data sets. In this study a variant of the SOM-algorithm is proposed, the GEO3DSOM, capable of explicitly incorporating three-dimensional spatial knowledge into the algorithm. The performance of the GEO3DSOM is compared to the performance of the standard SOM in analyzing an artificial data set and a hydrochemical data set. The hydrochemical data set consists of 141 groundwater samples collec

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Dragomir, Andrei, Seferina Mavroudi, and Anastasios Bezerianos. "Som-Based Class Discovery Exploring the ICA-Reduced Features of Microarray Expression Profiles." Comparative and Functional Genomics 5, no. 8 (2004): 596–616. http://dx.doi.org/10.1002/cfg.444.

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Gene expression datasets are large and complex, having many variables and unknown internal structure. We apply independent component analysis (ICA) to derive a less redundant representation of the expression data. The decomposition produces components with minimal statistical dependence and reveals biologically relevant information. Consequently, to the transformed data, we apply cluster analysis (an important and popular analysis tool for obtaining an initial understanding of the data, usually employed for class discovery). The proposed self-organizing map (SOM)-based clustering algorithm aut

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Galvin, T. J., M. T. Huynh, R. P. Norris, et al. "Cataloguing the radio-sky with unsupervised machine learning: a new approach for the SKA era." Monthly Notices of the Royal Astronomical Society 497, no. 3 (2020): 2730–58. http://dx.doi.org/10.1093/mnras/staa1890.

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ABSTRACT We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting Parallelized rotation and flipping INvariant Kohonen maps (pink), a self-organizing map (SOM) algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using 894 415 images from the Faint Images of the Radio-Sky at Twenty centimeters (FIRST) and Wide-field Infrared Survey Explorer (WISE) surveys centred towa

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