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Journal articles on the topic 'Growing Self Organizing Map'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Allahyar, Amin, Hadi Sadoghi Yazdi, and Ahad Harati. "Constrained Semi-Supervised Growing Self-Organizing Map." Neurocomputing 147 (January 2015): 456–71. http://dx.doi.org/10.1016/j.neucom.2014.06.039.

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2

Mehrizi, Ali, Hadi Sadoghi Yazdi, and Amir Hossein Taherinia. "Robust Semi-Supervised Growing Self-Organizing Map." Expert Systems with Applications 105 (September 2018): 23–33. http://dx.doi.org/10.1016/j.eswa.2018.03.046.

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3

Villmann, Th, and H. U. Bauer. "Applications of the growing self-organizing map." Neurocomputing 21, no. 1-3 (1998): 91–100. http://dx.doi.org/10.1016/s0925-2312(98)00037-x.

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Pramanik, Anima, Sobhan Sarkar, J. Maiti, and Pabitra Mitra. "RT-GSOM: Rough tolerance growing self-organizing map." Information Sciences 566 (August 2021): 19–37. http://dx.doi.org/10.1016/j.ins.2021.01.039.

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5

Murakoshi, Kazushi, and Satoshi Fujikawa. "Growing Hierarchical Self-Organizing Map Using Category Utility." International Journal of Software Engineering and Knowledge Engineering 26, no. 02 (2016): 217–37. http://dx.doi.org/10.1142/s0218194016500108.

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In order to automatically obtain hierarchical knowledge representation from a certain data, an unsupervised learning method has been developed that overcomes two problems of the growing hierarchical self-organizing map (GHSOM) method, which uses the quantization error, the deviation of the input data, as evaluation measure of the growing maps: proper control of the growth process of each map is difficult due to the use of the quantization error and the clusters in the hierarchical structure may be excessively subdivided. This improved GHSOM method uses the category utility (CU), a measure used

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6

Liao, Guang Lan, Tie Lin Shi, and Zi Rong Tang. "Gearbox Failure Detection Using Growing Hierarchical Self-Organizing Map." Key Engineering Materials 348-349 (September 2007): 177–80. http://dx.doi.org/10.4028/www.scientific.net/kem.348-349.177.

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Machine fault diagnosis is essentially an issue of pattern recognition, which heavily depends on suitable unsupervised learning method. The Self-Organizing Map (SOM), a popular unsupervised neural network, has been used for failure detection but with two limitations: needing predefined static architecture and lacking ability for the representation of hierarchical relations in the data. This paper presents a novel study on failure detection of gearbox using the Growing Hierarchical Self-Organizing Map (GHSOM), an artificial neural network model with hierarchical architecture composed of indepen

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Li, Baozhong, Yanming Liu, and Hailin Li. "Position Estimation Based on Grid Cells and Self-Growing Self-Organizing Map." Computational Intelligence and Neuroscience 2019 (February 26, 2019): 1–10. http://dx.doi.org/10.1155/2019/3606397.

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As the basis of animals’ natal homing behavior, path integration can continuously provide current position information relative to the initial position. Some neurons in freely moving animals’ brains can encode current positions and surrounding environments by special firing patterns. Research studies show that neurons such as grid cells (GCs) in the hippocampus of animals’ brains are related to the path integration. They might encode the coordinate of the animal’s current position in the same way as the residue number system (RNS) which is based on the Chinese remainder theorem (CRT). Hence, i

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Chattopadhyay, Manojit, Pranab K. Dan, and Sitanath Mazumdar. "Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system." Applied Soft Computing 22 (September 2014): 528–43. http://dx.doi.org/10.1016/j.asoc.2014.04.027.

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9

GUO, XIAOLIAN, HAIYING WANG, and DAVID H. GLASS. "BAYESIAN SELF-ORGANIZING MAP FOR DATA CLASSIFICATION AND CLUSTERING." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 05 (2013): 1350037. http://dx.doi.org/10.1142/s0219691313500379.

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The Bayesian self-organizing map (BSOM) has typically been used for density estimation. In this study, we implemented an adaptation of the model for performing unsupervized and supervised classification. In order to determine the optimal number of neurons to represent the given dataset during the learning process, an extended Bayesian learning process is proposed called the growing BSOM. It starts with two neurons and adds new neurons to the network via a process in which the neuron with the lowest individual log-likelihood is identified. The system has been tested using three synthetic datase

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10

Tomi, Yahya Christyawan, Afif Supianto Ahmad, and Firdaus Mahmudy Wayan. "Anomaly-based intrusion detector system using restricted growing self organizing map." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 919–26. https://doi.org/10.11591/ijeecs.v13.i3.pp919-926.

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The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with clustering reference v

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Christyawan, Tomi Yahya, Ahmad Afif Supianto, and Wayan Firdaus Mahmudy. "Anomaly-based intrusion detector system using restricted growing self organizing map." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 919. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp919-926.

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<p><span>The rapid development of internet and network technology followed by malicious threats and attacks on networks and computers. Intrusion detection system (IDS) was developed to solve that problems. The development of IDS using machine learning is needed for classifying the attacks. One method of the classification is Self-Organizing Map (SOM). SOM able to perform classification and visualization in learning process to gain new knowledge. However, the SOM has less efficient in learning process when applied in Big Data. This study proposes Restricted Growing SOM method with c

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12

Alharbe, Nawaf. "Growing Self-Organizing Map through a Hybrid Algorithm of Load Prediction." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1 (2020): 91–97. http://dx.doi.org/10.30534/ijatcse/2020/15912020.

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13

D.Patil, Dipti, and Prachi Gupta. "Growing Hierarchical Self-Organizing Map (GHSOM) for Mining Gene Expression Data." International Journal of Computer Applications 109, no. 2 (2015): 16–17. http://dx.doi.org/10.5120/19160-0603.

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14

Mo, Lingfei, Hongjie Yu, and Wenqi Hua. "Pruning Growing Self-Organizing Map Network for Human Physical Activity Identification." Journal of Healthcare Engineering 2022 (January 3, 2022): 1–15. http://dx.doi.org/10.1155/2022/9972406.

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Human physical activity identification based on wearable sensors is of great significance to human health analysis. A large number of machine learning models have been applied to human physical activity identification and achieved remarkable results. However, most human physical activity identification models can only be trained based on labeled data, and it is difficult to obtain enough labeled data, which leads to weak generalization ability of the model. A Pruning Growing SOM model is proposed in this paper to address the limitations of small-scale labeled dataset, which is unsupervised in

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15

Bauer, H. U., and T. Villmann. "Growing a hypercubical output space in a self-organizing feature map." IEEE Transactions on Neural Networks 8, no. 2 (1997): 218–26. http://dx.doi.org/10.1109/72.557659.

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16

Wu, Sitao, M. K. M. Rahman, and Tommy W. S. Chow. "Content-based image retrieval using growing hierarchical self-organizing quadtree map." Pattern Recognition 38, no. 5 (2005): 707–22. http://dx.doi.org/10.1016/j.patcog.2004.10.005.

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17

Montazeri, Hesam, Sajjad Moradi, and Reza Safabakhsh. "Continuous state/action reinforcement learning: A growing self-organizing map approach." Neurocomputing 74, no. 7 (2011): 1069–82. http://dx.doi.org/10.1016/j.neucom.2010.11.012.

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18

Zhang, Yaping, Wenxiu Bu, Chang Su, Luyao Wang, and Han Xu. "Intrusion detection method based on improved growing hierarchical self-organizing map." Transactions of Tianjin University 22, no. 4 (2016): 334–38. http://dx.doi.org/10.1007/s12209-016-2737-4.

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19

Ouyang, Wenbin, Bugao Xu, and Xiaohui Yuan. "Color segmentation in multicolor images using node-growing self-organizing map." Color Research & Application 44, no. 2 (2018): 184–93. http://dx.doi.org/10.1002/col.22333.

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20

Zhang, Jing Dan, Wu Han Jiang, Jun Du, and Rui Chun Wang. "An Adaptive Growing Self-Organizing Tree Map for Brain MR Image Segmentation." Applied Mechanics and Materials 462-463 (November 2013): 255–58. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.255.

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Segmentation of brain tissues from magnetic resonance (MR) images plays a crucial role in medical image processing. In this paper, we propose an automatic unsupervised segmentation method integrating wavelet transform with self-organizing map for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of wavelet transform and spatial position information. Then, an adaptive growing self-organizing tree map (AGSOTM) is presented, which adaptively captures the complicated spatial layout of the individual tissues, and overcomes t

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21

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, i

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22

Rauber, A., D. Merkl, and M. Dittenbach. "The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data." IEEE Transactions on Neural Networks 13, no. 6 (2002): 1331–41. http://dx.doi.org/10.1109/tnn.2002.804221.

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23

Chattopadhyay, Manojit, Nityananda Das, Pranab K. Dan, and Sitanath Mazumdar. "Growing hierarchical self-organizing map computation approach for clustering in cellular manufacturing." Journal of the Chinese Institute of Industrial Engineers 29, no. 3 (2012): 181–92. http://dx.doi.org/10.1080/10170669.2012.665396.

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24

Dittenbach, Michael, Andreas Rauber, and Dieter Merkl. "Uncovering hierarchical structure in data using the growing hierarchical self-organizing map." Neurocomputing 48, no. 1-4 (2002): 199–216. http://dx.doi.org/10.1016/s0925-2312(01)00655-5.

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25

Tai, Wei-Shen, and Chung-Chian Hsu. "Growing Self-Organizing Map with cross insert for mixed-type data clustering." Applied Soft Computing 12, no. 9 (2012): 2856–66. http://dx.doi.org/10.1016/j.asoc.2012.04.004.

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26

WU, ZHENG, and GARY G. YEN. "A SOM PROJECTION TECHNIQUE WITH THE GROWING STRUCTURE FOR VISUALIZING HIGH-DIMENSIONAL DATA." International Journal of Neural Systems 13, no. 05 (2003): 353–65. http://dx.doi.org/10.1142/s0129065703001662.

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The Self-Organizing Map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, an intuitive and effective SOM projection method is proposed for mapping high-dimensional data onto the two-dimensional grid structure with a growing self-organizing mechanism. In the learning phase, a growing SOM is trained and the growing cell structure is used as the baseline framework. In the ordination phase, the new projection method is used to map the input vector so that the input data is mapped to the structure of the SOM without having to plot the weight values, resulting in easy

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27

Mole, Vilson Luiz Dalle, and Aluizio Fausto Ribeiro Araújo. "Growing Self-Organizing Surface Map: Learning a Surface Topology from a Point Cloud." Neural Computation 22, no. 3 (2010): 689–729. http://dx.doi.org/10.1162/neco.2009.08-08-842.

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The growing self-organizing surface map (GSOSM) is a novel map model that learns a folded surface immersed in a 3D space. Starting from a dense point cloud, the surface is reconstructed through an incremental mesh composed of approximately equilateral triangles. Unlike other models such as neural meshes (NM), the GSOSM builds a surface topology while accepting any sequence of sample presentation. The GSOSM model introduces a novel connection learning rule called competitive connection Hebbian learning (CCHL), which produces a complete triangulation. GSOSM reconstructions are accurate and often

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28

Hong, Y., Y. ‐M Chiang, Y. Liu, K. ‐L Hsu, and S. Sorooshian. "Satellite‐based precipitation estimation using watershed segmentation and growing hierarchical self‐organizing map." International Journal of Remote Sensing 27, no. 23 (2006): 5165–84. http://dx.doi.org/10.1080/01431160600763428.

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29

Guru, Siddeswara Mayura, Arthur Hsu, Saman Halgamuge, and Saman Fernando. "An Extended Growing Self-Organizing Map for Selection of Clusters in Sensor Networks." International Journal of Distributed Sensor Networks 1, no. 2 (2005): 227–43. http://dx.doi.org/10.1080/15501320590966477.

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Sensor networks consist of wireless enabled sensor nodes with limited energy. As sensors could be deployed in a large area, data transmitting and receiving are energy consuming operations. One of the methods to save energy is to reduce the communication distance of each node by grouping them in to clusters. Each cluster will have a cluster-head (CH), which will communicate with all the other nodes of that cluster and transmit the data to the remote base station. In this paper, we propose an extension to Growing Self-Organizing Map (GSOM) and describe the use of evolutionary computing technique

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30

Ippoliti, Dennis, and Xiaobo Zhou. "A-GHSOM: An adaptive growing hierarchical self organizing map for network anomaly detection." Journal of Parallel and Distributed Computing 72, no. 12 (2012): 1576–90. http://dx.doi.org/10.1016/j.jpdc.2012.09.004.

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31

Malondkar, Ameya, Roberto Corizzo, Iluju Kiringa, Michelangelo Ceci, and Nathalie Japkowicz. "Spark-GHSOM: Growing Hierarchical Self-Organizing Map for large scale mixed attribute datasets." Information Sciences 496 (September 2019): 572–91. http://dx.doi.org/10.1016/j.ins.2018.12.007.

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32

Pham, Tung Son, Huy Minh Truong, and Tuan Ba Pham. "Application of self organizing map in construction, geology and petroleum industry." Science and Technology Development Journal 20, K4 (2017): 30–38. http://dx.doi.org/10.32508/stdj.v20ik4.1110.

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In recent years, Artificial Intelligence (AI) has become an emerging subject and been recognized as the flagship of the Fourth Industrial Revolution. AI is subtly growing and becoming vital in our daily life. Particularly, Self-Organizing Map (SOM), one of the major branches of AI, is a useful tool for clustering data and has been applied successfully and widespread in various aspects of human life such as psychology, economic, medical and technical fields like mechanical, construction and geology. In this paper, the primary purpose of the authors is to introduce SOM algorithm and its practica

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33

Shah-Hosseini, H., and R. Safabakhsh. "Automatic multilevel thresholding for image segmentation by the growing time adaptive self-organizing map." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 10 (2002): 1388–93. http://dx.doi.org/10.1109/tpami.2002.1039209.

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34

Kuo, R. J., M. Rizki, Ferani E. Zulvia, and A. U. Khasanah. "Integration of growing self-organizing map and bee colony optimization algorithm for part clustering." Computers & Industrial Engineering 120 (June 2018): 251–65. http://dx.doi.org/10.1016/j.cie.2018.04.044.

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35

Vasighi, Mahdi, and Homa Amini. "A directed batch growing approach to enhance the topology preservation of self-organizing map." Applied Soft Computing 55 (June 2017): 424–35. http://dx.doi.org/10.1016/j.asoc.2017.02.015.

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36

Tsui, I.-Fong, and Chau-Ron Wu. "Variability analysis of Kuroshio intrusion through Luzon Strait using growing hierarchical self-organizing map." Ocean Dynamics 62, no. 8 (2012): 1187–94. http://dx.doi.org/10.1007/s10236-012-0558-0.

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37

Seiffert, Udo, and Bernd Michaelis. "Growing 3D-SOMs with 2D-Input Layer as a Classification Tool in a Motion Detection System." International Journal of Neural Systems 08, no. 01 (1997): 81–89. http://dx.doi.org/10.1142/s0129065797000112.

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This paper describes the employment of an 'Adaptive Growing Three-Dimensional Self-Organizing Map' for the classification of images. First a short description of growing SOMs is given and the fundamental advantages are mentioned. Then an extension of the original SOM from two to three dimensions with growing feature is presented. By means of some selected examples the general behavior of the algorithm is illustrated.

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38

Johnsson, Magnus, and Christian Balkenius. "Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand." Journal of Robotics 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/860790.

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We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different size

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YuHsiang Yang, RuaHuan Tsaih, and Huimin Bhikshu. "The Research of Multi-Layer Topic Map Analysis using Co-word Analysis with Growing Hierarchical Self-organizing Map." International Journal of Digital Content Technology and its Applications 5, no. 3 (2011): 355–63. http://dx.doi.org/10.4156/jdcta.vol5.issue3.35.

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40

Ahmad, Norashikin, Damminda Alahakoon, and Rowena Chau. "Cluster identification and separation in the growing self-organizing map: application in protein sequence classification." Neural Computing and Applications 19, no. 4 (2009): 531–42. http://dx.doi.org/10.1007/s00521-009-0300-0.

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41

Shankar, Mahesh, Palaniappan Ramu, and Kalyanmoy Deb. "A directed batch growing self-organizing map based niching differential evolution for multimodal optimization problems." Applied Soft Computing 172 (March 2025): 112862. https://doi.org/10.1016/j.asoc.2025.112862.

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42

Tachibana, Kanta, Norihiko Sugimoto, Hideo Shiogama, and Toru Nozawa. "Visualization of Huge Climate Data with High-Speed Spherical Self-Organizing Map." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (2009): 210–16. http://dx.doi.org/10.20965/jaciii.2009.p0210.

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We propose the use of a high-speed spherical self-organizing map (HSS-SOM) to visualize climate variability as a complementary alternative to empirical orthogonal function (EOF) analysis. EOF analysis, which is the same as principal component analysis, is often used in the fields of meteorology and climatology to extract leading climate variability patterns, its production of linear mapping with only a low contribution rate may preclude producing any meaningful results. Due to computational limitations, however, conventional self-organizing maps are difficult to apply to huge climate datasets.

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43

Santhanaraj, Karthik, Dinakaran Devaraj, Ramya MM, Joshuva Dhanraj, and Kuppan Ramanathan. "Biologically Inspired Self-Organizing Computational Model to Mimic Infant Learning." Machine Learning and Knowledge Extraction 5, no. 2 (2023): 491–511. http://dx.doi.org/10.3390/make5020030.

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Recent technological advancements have fostered human–robot coexistence in work and residential environments. The assistive robot must exhibit humane behavior and consistent care to become an integral part of the human habitat. Furthermore, the robot requires an adaptive unsupervised learning model to explore unfamiliar conditions and collaborate seamlessly. This paper introduces variants of the growing hierarchical self-organizing map (GHSOM)-based computational models for assistive robots, which constructs knowledge from unsupervised exploration-based learning. Traditional self-organizing ma

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Liu, Yonggang, Robert H. Weisberg, and Ruoying He. "Sea Surface Temperature Patterns on the West Florida Shelf Using Growing Hierarchical Self-Organizing Maps." Journal of Atmospheric and Oceanic Technology 23, no. 2 (2006): 325–38. http://dx.doi.org/10.1175/jtech1848.1.

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Abstract Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned

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Zhong, Chaoliang, Shirong Liu, Qiang Lu, and Botao Zhang. "Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network." International Journal of Advanced Robotic Systems 14, no. 6 (2017): 172988141774361. http://dx.doi.org/10.1177/1729881417743612.

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Notsu, Akira, Koji Yasuda, Seiki Ubukata, and Katsuhiro Honda. "Online state space generation by a growing self-organizing map and differential learning for reinforcement learning." Applied Soft Computing 97 (December 2020): 106723. http://dx.doi.org/10.1016/j.asoc.2020.106723.

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47

Kuo, R. J., C. F. Wang, and Z. Y. Chen. "Integration of growing self-organizing map and continuous genetic algorithm for grading lithium-ion battery cells." Applied Soft Computing 12, no. 8 (2012): 2012–22. http://dx.doi.org/10.1016/j.asoc.2012.01.018.

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48

Pang, Tao, Xiao Gang Ruan, Er Shen Wang, and Rui Yuan Fan. "Search and Rescue Robot Path Planning in Unknown Environment." Applied Mechanics and Materials 241-244 (December 2012): 1682–87. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1682.

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For the path planning problem of search and rescue robot in unknown environment, a bionic learning algorithm was proposed. The GSOM (Growing Self-organizing Map) algorithm was used to build the environment cognitive map. The heuristic search A* algorithm was used to find the global optimal path from initial state to target state. When the local environment was changed, reinforcement learning algorithm based on sensor information was used to guide the search and rescue robot behavior of local path planning. Simulation results show the method effectiveness.

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Ellouze, Mehdi. "Social Network Community Detection by Combining Self-Organizing Maps and Genetic Algorithms." Complexity 2021 (October 21, 2021): 1–14. http://dx.doi.org/10.1155/2021/6699130.

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Social networks have become an important source of information from which we can extract valuable indicators that can be used in many fields such as marketing, statistics, and advertising among others. To this end, many research works in the literature offer users some tools that can help them take advantage of this mine of information. Community detection is one of these tools and aims to detect a set of entities that share some features within a social network. We have taken part in this effort, and we proposed an approach mainly based on pattern recognition techniques. The novelty of this a

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50

George Karimpanal, Thommen, and Roland Bouffanais. "Self-organizing maps for storage and transfer of knowledge in reinforcement learning." Adaptive Behavior 27, no. 2 (2018): 111–26. http://dx.doi.org/10.1177/1059712318818568.

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The idea of reusing or transferring information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency of a reinforcement learning agent. In this work, we describe a novel approach for reusing previously acquired knowledge by using it to guide the exploration of an agent while it learns new tasks. In order to do so, we employ a variant of the growing self-organizing map algorithm, which is trained using a measure of similarity that is defined directly in the space of the vectorized representatio

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