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

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Published: 4 June 2025

Last updated: 23 June 2025

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1

Moonlight, Lady Silk, Fiqqih Faizah, Yuyun Suprapto, and Nyaris Pambudiyatno. "Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition." Journal of Information Systems Engineering and Business Intelligence 7, no. 2 (2021): 149. http://dx.doi.org/10.20473/jisebi.7.2.149-161.

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Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning

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Begum, Momotaz, Bimal Chandra Das, Md Zakir Hossain, Antu Saha, and Khaleda Akther Papry. "An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 600. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp600-610.

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<p>Manipulating high-dimensional data is a major research challenge in the ﬁeld of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modiﬁed KSOM in terms of predictive performance with topographic and quantization error.</p>

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Begum, Momotaz, Bimal Chandra Das, Md. Zakir Hossain, Antu Saha, and Khaleda Akther Papry. "An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 600–610. https://doi.org/10.11591/ijeecs.v24.i1.pp600-610.

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Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.

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Batista, Milana Aboboreira Simões, Luana Novaes Santos, Bruna Cirineu Chagas, et al. "Artificial neural network employment for element determination in Mugil cephalus by ICP OES in Pontal Bay, Brazil." Analytical Methods 12, no. 29 (2020): 3713–21. http://dx.doi.org/10.1039/d0ay00799d.

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Mixture design applied to sample preparation of Mugil cephalus and exploratory evaluation of the concentrations of chemical elements using Kohonen Self-Organizing Map (KSOM) combined with Artificial Neural Network (ANNs).

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Prof.Yousef, Saleh Abuzir |. أ.د. يوسف صالح يوسف ابو زر, та Yousef Abuzir |. د. صالح يوسف أبو زر Dr.Saleh. "Data Mining Techniques for Prediction of Concrete Compressive Strength (CCS) | تقنيات التنقيب في البيانات للتنبؤ بالقوة الانضغاطية الخرسانية". Palestinian Journal of Technology & Applied Sciences | المجلة الفلسطينية للتكنولوجيا والعلوم التطبيقية, № 3 (17 лютого 2020): 57–72. https://doi.org/10.5281/zenodo.3672763.

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  Abstract The main aim of this research is to use data mining techniques to explore the main factors affecting the strength of concrete mix. In this research, we are interested in finding some of the factors that influence the high performance of concrete to increase the Concrete Compressive Strength (CCS) mix. We used Waikato’s Knowledge Analysis Environment (WEKA) tool and algorithms such as K-Means, Kohonen’s Self Organizing Map (KSOM) and EM to identify the most influential factors that increase the strength of the concrete mix. The results of this research showed that EM is highly capable of determining the main components that affect the compressive strength of high performance concrete mix. The other two algorithms, K-Means and KSOM, were noted to be an advanced predictive model for predicting the strength of the concrete mix.   تقنيات التنقيب في البيانات للتنبؤ بالقوة الانضغاطية الخرسانية ملخص: هدف البحث الرئيس، هو استخدام تقنيات استخراج البيانات لاكتشاف العوامل الرئيسية التي تؤثر في قوة مزيج الخرسانة. إن جل اهتمامنا في هذا البحث، هو إيجاد بعض العوامل التي تؤثر في الأداء العالي للخرسانة لزيادة مزيج قوة ضاغطة الخرسانة. لتحقيق هذا الهدف، استخدمنا أداة Waikato’s Environment Analysis Environment (WEKA) وخوارزميات مثل K-Means وخريطة كوهن ذاتية التنظيم (KSOM) و EM لتحديد العوامل الأكثر تأثير واًلتي تزيد من قوة مزيج الخرسانة. أظهرت نتائج هذا البحث أن EM يظهر أهمية كبيرة لتحديد المكونات الرئيسية التي تؤثر في قوة الضغط للمزيج الخرساني عالي الأداء. بينما تعد الخوارزميات K-Means و KSOM نموذجًا تنبؤيًا متقدمًا لقوة الخلطة الخرسانية. كلمات مفتاحية: تعدين البيانات، قوة الضغط الخرسانية (CCS)،K-means، EM خوارزمية ، خريطة كوهن ذاتية التنظيم (KSOM).

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Du, Zhan Wei, Yong Jian Yang, Yong Xiong Sun, and Chi Jun Zhang. "Map Matching Using De-Noise Interpolation Kohonen Self-Organizing Maps." Key Engineering Materials 460-461 (January 2011): 680–86. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.680.

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In this work, we have proposed a de-noise interpolation Kohonen Self-Organizing Maps(DNIKSOM) -based method for the Map matching(MM). It has been seen that there are some problems in the MM, such as large error range of the original position information, low match accuracy and so on. Therefore, in MM problem to achieve high accuracy, it is necessary to consider the topography of roads and the requirement for match accuracy lying within the original position information in the matching process. In the present study, Kohonen Self-Organizing Maps(KSOM) in the field of pattern recognition possesses good performance. Now to get more valuable position information, A kind of de-noise algorithm for Kohonen neural network is proposed to meet the case that neural network may not be trained sufficiently with consideration for the topography of roads. And a kind of Lagrange interpolation algorithm is also proposed to meet the requirements for matching accuracy. These processes make the amended position information closer to the true value. In this application to a city’s MM, we investigate DNIKSOM’s ,KSOM’s and Centroid localization algorithm’s location performance on a original position data set. Finally, the comparison of experimental results shows that DNIKSOM has better location performance than others.

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Sahu, Ranu, and Khushboo Choubey. "Comparative Analysis of Supervised and Unsupervised Learning Methods for Pattern Classification." International Journal of Innovative Research in Computer and Communication Engineering 12, Special Is (2024): 58–63. http://dx.doi.org/10.15680/ijircce.2024.1203509.

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In the higher learning system, this article compares and contrasts supervised and unsupervised learning approaches to see which is more effective for classifying patterns. Among the most significant uses of machine learning algorithms is classification. Our research shows that, although the supervised learning algorithm, Backpropagation learning with errors, does a great deal of nonlinear real-time assignments, the unsupervised learning algorithm, Kohonen Self-Organizing Map (KSOM), performs very well in our study's classification tasks.

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Nanda, Trushnamayee, Bhabagrahi Sahoo, and Chandranath Chatterjee. "Enhancing the applicability of Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in hydrometeorological timeseries data." Journal of Hydrology 549 (June 2017): 133–47. http://dx.doi.org/10.1016/j.jhydrol.2017.03.072.

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Mohanty, Mahamaya, Rashmi Singh, and Ravi Shankar. "Improving the operational efficiency of outbound retail logistics using clustering of retailers and consumers." Journal of Modelling in Management 13, no. 3 (2018): 646–74. http://dx.doi.org/10.1108/jm2-12-2016-0137.

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Purpose The purpose of this paper is to investigate ways to improve operational efficiency of outbound retail logistics considering retailers and consumers by using clustering approach. The retailers are allocated to serve a cluster of consumers. This study demonstrates economic and environment benefits that are achieved in terms of reduced delivery time, transportation cost and carbon emissions. Design/methodology/approach This study is based on modeling the outbound logistics of a retail chain by using Kohonen self-organizing map (KSOM). KSOM is an unsupervised learning and data analysis method for vector quantization, which is based on Euclidean distance method to form clusters. Findings Appropriate clustering of retailers and consumers provides efficient locations of retailers that are identified using the KSOM training algorithm. It provides optimum distance with lesser delivery time, transportation cost and carbon emissions. Research limitations/implications The implication of research includes modeling of operational procedures in a retail supply chain, which is a crucial task for a business. These operations positively affect the reduction in inventory and distribution costs, improvement in customer service and responsiveness to the ever-changing markets of consumer durables. Overall results are insightful and practical in the sense that implementation would result in consumer convenience, eco-friendly environment, etc. Originality/value There is not enough research available on outbound retail logistics considering retailers and consumers using clustering approach.

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Ferde, Merisnet, Vinicius Câmara Costa, Rafael Mantovaneli, et al. "Chemical characterization of the soils from black pepper (Piper nigrum L.) cultivation using principal component analysis (PCA) and Kohonen self-organizing map (KSOM)." Journal of Soils and Sediments 21, no. 9 (2021): 3098–106. http://dx.doi.org/10.1007/s11368-021-02966-3.

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Jangid, Hitesh, Subham Jain, Beteley Teka, Rekha Raja, and Ashish Dutta. "Kinematics-based end-effector path control of a mobile manipulator system on an uneven terrain using a two-stage Support Vector Machine." Robotica 38, no. 8 (2019): 1415–33. http://dx.doi.org/10.1017/s0263574719001541.

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SUMMARYA mobile manipulator system (MMS) consists of a robotic arm mounted on a mobile platform that is used in rescue and relief, space exploration, warehouse automation, etc. As the total system has 14 Degrees of Freedom (DOF), it does not have a closed-form inverse kinematics (IK) solution. A learning-based method is proposed, which uses the forward kinematics data to learn the IK relation for motion of an MMS on a rough terrain, using a one-class support vector machine (SVM) framework. Once trained, the model estimates the joint probability distribution of the MMS configuration and end-effector position. This distribution is used to find the MMS configuration for a given desired end-effector path. Past research using a Kohonen Self organizing map (KSOM) neural network-based open-loop control method has shown that the MMS deviates from its desired path while moving on an uneven terrain due to unknown disturbances such as wheel slip, slide, and terrain deformation. Therefore, a new sequential two-stage SVM-based end-effector path-tracking control scheme is proposed to control the end-effector path. In this scheme, the error in the end-effector path is continuously tracked with the help of a Microsoft Kinect 2.0 (Microsoft Regional Sales, Singapore 119968) and is sent as a feedback to the controller. Once the error reaches a threshold value, the error correction step of the controller gets activated to correct the error until the desired accuracy is reached. The effectiveness of the proposed approach is proved through extensive simulations and experiments conducted on 3D terrain in which it is shown that the end effector can follow the desired path with an average experimental error of around 2 cm between the desired and final corrected path.

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Velzen, G. A. van. "Instabilities in Kohonen's self-organizing feature map." Journal of Physics A: Mathematical and General 27, no. 5 (1994): 1665–81. http://dx.doi.org/10.1088/0305-4470/27/5/029.

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13

Cheng, Yizong. "Convergence and Ordering of Kohonen's Batch Map." Neural Computation 9, no. 8 (1997): 1667–76. http://dx.doi.org/10.1162/neco.1997.9.8.1667.

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The convergence and ordering of Kohonen's batch-mode self-organizing map with Heskes and Kappen's (1993) winner selection are proved. Selim and Ismail's (1984) objective function for k-means clustering is generalized in the convergence proof of the self-organizing map. It is shown that when the neighborhood relation is doubly decreasing, order in the map is preserved. An unordered map becomes ordered when a degenerate state of ordering is entered, where the number of distinct winners is one or two. One strategy to enter this state is to run the algorithm with a broad neighborhood relation.

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Naylor, J., A. Higgins, K. P. Li, and D. Schmoldt. "Speaker recognition using kohonen's self-organizing feature map algorithm." Neural Networks 1 (January 1988): 311. http://dx.doi.org/10.1016/0893-6080(88)90342-5.

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Patra, Chiranjib. "Using Kohonen's Self-Organizing Map for Clustering in Sensor Networks." International Journal of Computer Applications 1, no. 24 (2010): 94–95. http://dx.doi.org/10.5120/552-722.

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Elfadil, Nazar. "Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 6 (2005): 693–97. http://dx.doi.org/10.20965/jaciii.2005.p0693.

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Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

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Bauer, H. U., and R. Der. "Controlling the Magnification Factor of Self-Organizing Feature Maps." Neural Computation 8, no. 4 (1996): 757–71. http://dx.doi.org/10.1162/neco.1996.8.4.757.

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The magnification exponents μ occurring in adaptive map formation algorithms like Kohonen's self-organizing feature map deviate for the information theoretically optimal value μ = 1 as well as from the values that optimize, e.g., the mean square distortion error (μ = 1/3 for one-dimensional maps). At the same time, models for categorical perception such as the "perceptual magnet" effect, which are based on topographic maps, require negative magnification exponents μ < 0. We present an extension of the self-organizing feature map algorithm, which utilizes adaptive local learning step sizes to actually control the magnification properties of the map. By change of a single parameter, maps with optimal information transfer, with various minimal reconstruction errors, or with an inverted magnification can be generated. Analytic results on this new algorithm are complemented by numerical simulations.

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Borkowska, E. M., A. Kruk, A. Jedrzejczyk, et al. "995 Kohonen's self-organizing map for molecular subtyping in bladder cancer." European Urology Supplements 13, no. 1 (2014): e995. http://dx.doi.org/10.1016/s1569-9056(14)60978-7.

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Yin, Hujun, and Nigel M. Allinson. "On the Distribution and Convergence of Feature Space in Self-Organizing Maps." Neural Computation 7, no. 6 (1995): 1178–87. http://dx.doi.org/10.1162/neco.1995.7.6.1178.

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In this paper an analysis of the statistical and the convergence properties of Kohonen's self-organizing map of any dimension is presented. Every feature in the map is considered as a sum of a number of random variables. We extend the Central Limit Theorem to a particular case, which is then applied to prove that the feature space during learning tends to multiple gaussian distributed stochastic processes, which will eventually converge in the mean-square sense to the probabilistic centers of input subsets to form a quantization mapping with a minimum mean squared distortion either globally or locally. The diminishing effect, as training progresses, of the initial states on the value of the feature map is also shown.

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Graepel, Thore, and Klaus Obermayer. "A Stochastic Self-Organizing Map for Proximity Data." Neural Computation 11, no. 1 (1999): 139–55. http://dx.doi.org/10.1162/089976699300016854.

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We derive an efficient algorithm for topographic mapping of proximity data (TMP), which can be seen as an extension of Kohonen's self-organizing map to arbitrary distance measures. The TMP cost function is derived in a Baysian framework of folded Markov chains for the description of autoencoders. It incorporates the data by a dissimilarity matrix [Formula: see text] and the topographic neighborhood by a matrix [Formula: see text] of transition probabilities. From the principle of maximum entropy, a nonfactorizing Gibbs distribution is obtained, which is approximated in a mean-field fashion. This allows for maximum likelihood estimation using an expectation-maximization algorithm. In analogy to the transition from topographic vector quantization to the self-organizing map, we suggest an approximation to TMP that is computationally more efficient. In order to prevent convergence to local minima, an annealing scheme in the temperature parameter is introduced, for which the critical temperature of the first phase transition is calculated in terms of [Formula: see text] and [Formula: see text]. Numerical results demonstrate the working of the algorithm and confirm the analytical results. Finally, the algorithm is used to generate a connection map of areas of the cat's cerebral cortex.

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Li, K. ‐P, and J. Naylor. "Analysis of Kohonen's self‐organizing feature map for vector quantization of speech." Journal of the Acoustical Society of America 84, S1 (1988): S14. http://dx.doi.org/10.1121/1.2025864.

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Yusof, Rubiyah, Azlin Ahmad, Nor Saradatul Akmar Zulkifli, and Mohd Najib Ismail. "An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets." Journal of Information and Communication Technology 20, No.4 (2020): 651–76. http://dx.doi.org/10.32890/jict2021.20.4.8.

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The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. The Kohonen Self-Organising Map (KSOM) is one of the well-known clustering algorithms that can solve various problems without a pre-defined number of clusters. However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. The inadequate amount of class label data in a dataset significantly affects the clustering learning process, leading to inefficient and unreliable results. Numerous research have been conducted by hybridising and optimising the KSOM algorithm with various optimisation techniques. Unfortunately, the problems are still unsolved, especially separation boundary and overlapping clusters. Therefore, this research proposed an improved pheromone- based PKSOM algorithm known as iPKSOM to solve the mentioned problem. Six different datasets, i.e. Iris, Seed, Glass, Titanic, WDBC, and Tropical Wood datasets were chosen to investigate the effectiveness of the iPKSOM algorithm. All datasets were observed and compared with the original KSOM results. This modification significantly impacted the clustering process by improving and refining the scatteredness of clustering data and reducing overlapping clusters. Therefore, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional and streaming data.

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Rivera-Rovelo, Jorge, and Eduardo Bayro-Corrochano. "Surface Approximation using Growing Self-Organizing Nets and Gradient Information." Applied Bionics and Biomechanics 4, no. 3 (2007): 125–36. http://dx.doi.org/10.1155/2007/502679.

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In this paper we show how to improve the performance of two self-organizing neural networks used to approximate the shape of a 2D or 3D object by incorporating gradient information in the adaptation stage. The methods are based on the growing versions of the Kohonen's map and the neural gas network. Also, we show that in the adaptation stage the network utilizes efficient transformations, expressed as versors in the conformal geometric algebra framework, which build the shape of the object independent of its position in space (coordinate free). Our algorithms were tested with several images, including medical images (CT and MR images). We include also some examples for the case of 3D surface estimation.

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Brockett, Patrick L., Xiaohua Xia, and Richard A. Derrig. "Using Kohonen's Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud." Journal of Risk and Insurance 65, no. 2 (1998): 245. http://dx.doi.org/10.2307/253535.

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De, A., and N. Chatterjee. "Recognition of Impulse Fault Patterns in Transformers Using Kohonen's Self-Organizing Feature Map." IEEE Power Engineering Review 22, no. 2 (2002): 64. http://dx.doi.org/10.1109/mper.2002.4312019.

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Marique, Thierry, Stephanie Pennincx, and Ammar Kharoubi. "Image Segmentation and Bruise Identification on Potatoes Using a Kohonen's Self-Organizing Map." Journal of Food Science 70, no. 7 (2005): e415-e417. http://dx.doi.org/10.1111/j.1365-2621.2005.tb11469.x.

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Ozdzynski, Piotr, Andy Lin, Mimi Liljeholm, and Jackson Beatty. "A parallel general implementation of Kohonen's self-organizing map algorithm: performance and scalability." Neurocomputing 44-46 (June 2002): 567–71. http://dx.doi.org/10.1016/s0925-2312(02)00427-7.

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Giuliano, F., P. Arrigo, F. Scalia, P. P. Cardo, and G. Damiani. "Potentially functional regions of nucleic acids recognized by a Kohonen's self-organizing map." Bioinformatics 9, no. 6 (1993): 687–93. http://dx.doi.org/10.1093/bioinformatics/9.6.687.

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De, A., and N. Chatterjee. "Recognition of impulse fault patterns in transformers using Kohonen's self-organizing feature map." IEEE Transactions on Power Delivery 17, no. 2 (2002): 489–94. http://dx.doi.org/10.1109/61.997923.

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Cheng, Ching-Hsue, and Ming-Chi Tsai. "An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices." Land 11, no. 8 (2022): 1138. http://dx.doi.org/10.3390/land11081138.

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Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations.

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Pakhomova, V., and A. Vydish. "Study of the combined variant of determination of attacks using neural network technologies." System technologies 3, no. 140 (2022): 79–86. http://dx.doi.org/10.34185/1562-9945-3-140-2022-08.

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The modern world is impossible to imagine without computer networks: both local and global; therefore, the issue of network security is becoming increasingly topical. Currently, methods of detecting attacks can be strengthened by using neural networks, which confirms the relevance of the topic. The aim of the study is a comparative analysis of the quality parameters of network attacks using a combined variant consisting of different neural networks. As research methods used: neural network; multilayer perceptron; Kohonen's self-organizing map. The software implementation of the Kohonen self-organizing map is carried out in Python with a wide range of modern standard tools, creation of a multilayer perceptron and a fuzzy network - using Neural Network Toolbox packages, and Fuzzy Logic Toolbox system MatLAB. On the created neural networks separately and on their combined variant researches of parameters of quality of definition of network attacks are carried out. It was determined that the error of the first kind was 11%, 4%, 10% and 0%, the error of the second kind - 7%, 6%, 9% and 6% on the fuzzy network, multilayer perceptron, self-organizing Kohonen map and their combined version, respectively, which proves the feasibility of using the combined option.

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Bataineh, Asia K., Mohammad Habib Samkari, Abdualla Abdualla, and Saad Al-Azzam. "K-Means Clustering in WSN with Koheneon SOM and Conscience Function." Modern Applied Science 13, no. 8 (2019): 63. http://dx.doi.org/10.5539/mas.v13n8p63.

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Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map&nbsp;(KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.

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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 neighborhood function corresponds to the kernel and neighborhood width corresponds to kernel span. To determine the new unit locations, kernel smoothing is applied to the centroids of the Voronoi regions in the topological space. This interpretation leads to some new insights concerning the role of the neighborhood and dimensionality reduction. It also strengthens the algorithm's connection with the Principal Curve algorithm. A generalized self-organizing algorithm is proposed, where the kernel smoothing step is replaced with an arbitrary nonparametric regression method.

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FUJIMURA, Kikuo, Heizo TOKUTAKA, Yasuhiro OHSHIMA, Shin-ichi TANAKA, and Satoru KISHIDA. "An Improvement of Algorithm using Kohonen's Self-Organizing Feature Map for the Traveling Salesman Problem." IEEJ Transactions on Electronics, Information and Systems 116, no. 3 (1996): 350–58. http://dx.doi.org/10.1541/ieejeiss1987.116.3_350.

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Arrigo, P., F. Giuliano, F. Scalia, A. Rapallo, and G. Damiani. "Identification of a new motif on nucleic acid sequence data using Kohonen's self-organizing map." Bioinformatics 7, no. 3 (1991): 353–57. http://dx.doi.org/10.1093/bioinformatics/7.3.353.

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Kikuchi, Shingo, Yoshinori Onuki, Akihito Yasuda, Yoshihiro Hayashi, and Kozo Takayama. "Latent Structure Analysis in Pharmaceutical Formulations Using Kohonen's Self-Organizing Map and a Bayesian Network." Journal of Pharmaceutical Sciences 100, no. 3 (2011): 964–75. http://dx.doi.org/10.1002/jps.22340.

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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 groups of the firms. Implications of this finding for investment decisions have been discussed.

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MEYER-BÄSE, ANKE, OLIVER LANGE, AXEL WISMÜLLER, and HELGE RITTER. "MODEL-FREE FUNCTIONAL MRI ANALYSIS USING TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS." International Journal of Neural Systems 14, no. 04 (2004): 217–28. http://dx.doi.org/10.1142/s0129065704002017.

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Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.

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Al-Khasawneh, Ahmad. "Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques." International Journal of E-Health and Medical Communications 7, no. 1 (2016): 65–75. http://dx.doi.org/10.4018/ijehmc.2016010104.

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Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

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Adamczyk, Jolanta J., Maria Kurzac, Young-Seuk Park, and Andrzej Kruk. "Application of a Kohonen's self-organizing map for evaluation of long-term changes in forest vegetation." Journal of Vegetation Science 24, no. 2 (2012): 405–14. http://dx.doi.org/10.1111/j.1654-1103.2012.01468.x.

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Swindale, N. V., and H. Bauer. "Application of Kohonen's self–organizing feature map algorithm to cortical maps of orientation and direction preference." Proceedings of the Royal Society of London. Series B: Biological Sciences 265, no. 1398 (1998): 827–38. http://dx.doi.org/10.1098/rspb.1998.0367.

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Loula, R., and L. H. A. Monteiro. "On the criteria for diagnosing depression in bereaved individuals: a self-organizing map approach." Mathematical Biosciences and Engineering 19, no. 6 (2022): 5380–92. http://dx.doi.org/10.3934/mbe.2022252.

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<abstract><p>Bereavement exclusion (BE) is a criterion for excluding the diagnosis of major depressive disorder (MDD). Simplistically, this criterion states that an individual who reports MDD symptoms should not be diagnosed as suffering from this mental illness, if such an individual is grieving a sorrowful loss. BE was introduced in 1980 to avoid confusing MDD with normal grief, because several cognitive and physical symptoms of grief and depression can look similar. However, in 2013, BE was removed from the MDD diagnosis guidelines. Here, this controversial topic is computationally investigated. A virtual population is generated according to the Brazilian data of death rate and MDD prevalence and its five kinds of individuals are clustered by using a Kohonen's self-organizing map (SOM). In addition, by examining the current guidelines for diagnosing MDD from an analytical perspective, a slight modification is proposed. With this modification, an adequate clustering is achieved by the SOM neural network. Therefore, for mathematical consistency, unbalanced scores should be assigned to the items composing the MDD diagnostic criteria. With the proposed criteria, the co-occurrence of normal grief and MDD can also be satisfactorily clustered.</p></abstract>

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Liu, Gui Song, Hong Qu, and Zhang Yi. "Norm-Profile Construction Using Splitting Neural Gas for Anomaly Detection." Advanced Materials Research 629 (December 2012): 826–31. http://dx.doi.org/10.4028/www.scientific.net/amr.629.826.

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A neural gas network is a single-layered soft competitive neural network, which has many advantages for clustering analysis comparing to Kohonen's self-organizing map, K-means etc. This paper proposes a splitting neural gas algorithm (SNG). By initializing neurons splitting and finally deleting operations, the SNG can be used to characterize a certain class pattern effectively. We utilize the SNG to construct the profile of normal activities for anomaly detection in network security. Simulations are carried out using KDD CUP intrusion detection evaluation datasets. The experimental results showed that the SNG classifier can achieve the detection rate higher than 99% with a false positive rate lower than 1.6% and outperform many other recent supervised or unsupervised approaches.

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Liljeholm, Mimi, Andy Lin, Piotr Ozdzynski, and Jackson Beatty. "Quantitative analysis of kernel properties in Kohonen's self-organizing map algorithm: Gaussian and difference of Gaussians neighborhoods." Neurocomputing 44-46 (June 2002): 515–20. http://dx.doi.org/10.1016/s0925-2312(02)00410-1.

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Li, Zhe, and J. Ronald Eastman. "The Nature and Classification of Unlabelled Neurons in the Use of Kohonen's Self-Organizing Map for Supervised Classification." Transactions in GIS 10, no. 4 (2006): 599–613. http://dx.doi.org/10.1111/j.1467-9671.2006.01014.x.

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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 avoid neighborhood violations up to a user-defined degree in either mapping direction. Auto-SOM consists of a Kalman filter implementation of the SOM coupled with a recursive parameter estimation method. The Kalman filter trains the neurons' weights with estimated learning coefficients so as to minimize the variance of the estimation error. The recursive parameter estimation method estimates the width of the neighborhood function by minimizing the prediction error variance of the Kalman filter. In addition, the “topographic function” is incorporated to measure neighborhood violations and prevent the map's converging to configurations with neighborhood violations. It is demonstrated that neighborhoods can be preserved in both mapping directions as desired for dimension-reducing applications. The development of neighborhood-preserving maps and their convergence behavior is demonstrated by three examples accounting for the basic applications of self-organizing feature maps.

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Yarovenko, Hanna, Serhiy Lyeonov, Krzysztof A. Wojcieszek, and Zoltán Szira. "Do IT users behave responsibly in terms of cybercrime protection?" Human Technology 19, no. 2 (2023): 178–206. http://dx.doi.org/10.14254/1795-6889.2023.19-2.3.

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This study aims to analyze the behaviour of IT users regarding their personal protection against potential cybercrimes. The research data set is based on surveys conducted by the European Commission in 2020-2021 for 35 European countries. Canonical analysis revealed that 66.67% of cybercrime cases (Phishing, Pharming, Online identity theft, etc.) determine individuals' choice of personal protection method (using a security token, social media logins, electronic identification, etc.). Kohonen's self-organizing maps were used to form 9 clusters of countries depending on the attitude of IT users to personal cybersecurity. The map results showed that individuals behave less responsibly using a security token, electronic identification certificate or card, pin code list or random characters of a password, and other electronic identification procedures. Users from Denmark, the Netherlands, Iceland, Norway, the UK, Austria, and Finland were the most responsible Europeans in terms of personal protection, while people from Bulgaria, Romania, Serbia, Albania, North Macedonia, Bosnia and Herzegovina were the least conscientious about protection.

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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 assembly modes. Each solution of the multiple feasible ones is coded using IF-THEN rules based on the values of the passive joint variables. Due to not only the classification but also the associative memory learning abilities of the SOM, the passive joint variables vector, the end-effector pose vector, and this class code are associated with the active joint variables vector constituting the input vector to the SOM in the offline learning phase. In the online testing phase, only the active joint variables vector and the class code are fed to the SOM to obtain the unique end-effector pose vector. The Jacobian matrix calculated at the SOM output layer is used for further fine tuning this output to obtain an accurate end-effector pose vector. Simulations are conducted for 3-RPR and 3-RRR planar parallel manipulators to evaluate the performance of the proposed method. The results proved high accuracy of the desired unique solution in real-time.

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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 11, no. 4 (2007): 1309–21. http://dx.doi.org/10.5194/hess-11-1309-2007.

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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 131 groundwater samples collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. Both techniques succeed very well in providing more insight in the groundwater quality data set, visualizing the relationships between variables, highlighting the main differences between groups of samples and pointing out anomalous wells and well screens. The GEO3DSOM however has the advantage to provide an increased resolution while still maintaining a good generalization of the data set.

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Liu, Baoling, Gang Li, Hong You, and Mingrui Sui. "Assessment of the surface water quality ranking in Mudan River using multivariate statistical techniques." Water Supply 15, no. 3 (2015): 606–16. http://dx.doi.org/10.2166/ws.2015.015.

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The present research deals with the important issue of assessing surface water quality ranking by combining the use of two advanced multivariate statistical techniques: Kohonen's self-organizing maps (SOM) and the Hasse diagram technique (HDT). The object of the study is the Mudan River of Mudanjiang city region, China. Samples were collected on a regular monthly basis in 2007–2011 from all sampling sites along the river, involving six major water quality parameters. The grouping of water parameters and the clustering of sampling events by the use of SOM has helped in their pre-processing for application of the HDT. The HDT orders clusters according to the pre-clustered water sampling events. The water quality was ranked against norms established by the Ministry of Environmental Protection of the People's Republic of China in order to assess in detail the water quality of the whole river system. The resulting map of the spatial and temporal changes in the water quality at each sampling site was specifically described by ArcGIS.

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