scholarly journals S-SOM v1.0: a structural self-organizing map algorithm for weather typing

2021 ◽  
Vol 14 (4) ◽  
pp. 2097-2111
Author(s):  
Quang-Van Doan ◽  
Hiroyuki Kusaka ◽  
Takuto Sato ◽  
Fei Chen
Keyword(s):  
Pearson Correlation ◽  
Structural Similarity ◽  
Self Organizing Map ◽  
Weather Patterns ◽  
Weather Typing ◽  
Synoptic Weather Typing ◽  
Clustering Quality ◽  
Som Algorithm ◽  
The Difference ◽  
Self Organizing

Abstract. This study proposes a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data with spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based on a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the locations of highs or lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the S-SOM's superiority compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error. Better performance of S-SOM versus ED is consistent with results from different tests and node-size configurations. S-SOM performs better than a SOM using the Pearson correlation coefficient (or COR-SOM), though the difference is not as clear as it is compared to ED-SOM.

scholarly journals A structural self-organizing map (S-SOM) algorithm for weather typing

2020 ◽  
Author(s):  
Quang-Van Doan ◽  
Hiroyuki Kusaka ◽  
Takuto Sato ◽  
Fei Chen
Keyword(s):  
Input Data ◽  
Structural Similarity ◽  
Superior Performance ◽  
Self Organizing Map ◽  
Weather Patterns ◽  
Weather Typing ◽  
Synoptic Weather Typing ◽  
Clustering Quality ◽  
Som Algorithm ◽  
Self Organizing

Abstract. In this study, we propose a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data that have spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based upon a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the location of highs of lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the superiority of the S-SOM compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error analysis. The superior performance of the S-SOM compared with an ED-SOM is probably independent of both the input data and SOM configuration.

scholarly journals Application of self organizing map in construction, geology and petroleum industry

2017 ◽  
Vol 20 (K4) ◽  
pp. 30-38
Author(s):  
Tung Son Pham ◽  
Huy Minh Truong ◽  
Tuan Ba Pham
Keyword(s):  
Industrial Revolution ◽  
Human Life ◽  
Petroleum Industry ◽  
Self Organizing Map ◽  
Practical Applications ◽  
Fourth Industrial Revolution ◽  
Som Algorithm ◽  
Clustering Data ◽  
Self Organizing

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 practical applications in geology and construction. The results are classification of rock facies versus depth in geology and clustering two sets of construction prices indices and building material costs indice.

scholarly journals Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6009
Author(s):  
Ignacio Sánchez Andrades ◽  
Juan J. Castillo Aguilar ◽  
Juan M. Velasco García ◽  
Juan A. Cabrera Carrillo ◽  
Miguel Sánchez Lozano
Keyword(s):  
Low Cost ◽  
Road Surface ◽  
Engineering Industry ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Driver Assistance Systems ◽  
Self Organizing Maps ◽  
Learning Techniques ◽  
Som Algorithm ◽  
Self Organizing

Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance.

Weight-Value Convergence of the SOM Algorithm for Discrete Input

1998 ◽  
Vol 10 (4) ◽  
pp. 807-814 ◽  
Author(s):  
Siming Lin ◽  
Jennie Si
Keyword(s):  
Stationary State ◽  
Convergence Result ◽  
The Self ◽  
Self Organizing Map ◽  
Input Output ◽  
Weight Value ◽  
Som Algorithm ◽  
Self Organizing

Some insights on the convergence of the weight values of the self-organizing map (SOM) to a stationary state in the case of discrete input are provided. The convergence result is obtained by applying the Robbins-Monro algorithm and is applicable to input-output maps of any dimension.

scholarly journals Implementasi Data Mining untuk mengelompokkan Pasien Rawat Jalan dan Rawat Inap Asuransi Kesehatan

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Ratih
Keyword(s):  
Data Mining ◽  
The Self ◽  
Self Organizing Map ◽  
Clustering Techniques ◽  
Dental Clinics ◽  
Som Algorithm ◽  
Outpatient Health Services ◽  
Internal Medicine Clinic ◽  
Eye Clinic ◽  
Self Organizing

Patient Visits Outpatient and inpatient insurance at Class C Hospitals is increasing from year to year. Increased visits to insurance patients will have an impact on the inpatient and outpatient health services provided. From the increase in patient visits, the data owned by the hospital is increasingly abundant. The data can be used to explore knowledge, find certain patterns. To explore knowledge about Inpatient and Outpatient Insurance patients, data mining clustering techniques are used with the Self Organizing Map (SOM) algorithm using R Studio tools. Clustering technique with the implementation of the Self Organizing Map (SOM) algorithm is a technique for grouping data based on certain characteristics which are then mapped into areas that resemble map shapes. The CRISP-DM method is used in this study to perform the stages of the data mining process. The results obtained from the implementation of clustering with the Self Organizing Map (SOM) algorithm are obtained 2 clusters representing dense areas and non-congested areas. Dense areas are represented by Internal Medicine Clinic, Surgery Clinic, Eye Clinic, Hemodialysis, Melati Room, Orchid Room, Bougenville Room, Flamboyan Room. Non-crowded areas are represented by General Clinics, Dental Clinics, Obstetrics and Gynecology Clinics, Children's Clinics, Mawar Room and Soka Room

scholarly journals Self-Organizing Map vs Initial Centroid Selection Optimization to Enhance K-Means with Genetic Algorithm to Cluster Transcribed Broadcast News Documents

2019 ◽  
Vol 17 (3) ◽  
pp. 316-324
Author(s):  
Ahmed Maghawry ◽  
Yasser Omar ◽  
Amr Badr
Keyword(s):  
Genetic Algorithm ◽  
Self Organizing Map ◽  
Optimal Solutions ◽  
Algorithm Optimization ◽  
Chi Square ◽  
Text Documents ◽  
Data Set ◽  
Average Accuracy ◽  
The Difference ◽  
Self Organizing

A compilation of artificial intelligence techniques are employed in this research to enhance the process of clustering transcribed text documents obtained from audio sources. Many clustering techniques suffer from drawbacks that may cause the algorithm to tend to sub optimal solutions, handling these drawbacks is essential to get better clustering results and avoid sub optimal solutions. The main target of our research is to enhance automatic topic clustering of transcribed speech documents, and examine the difference between implementing the K-means algorithm using our Initial Centroid Selection Optimization (ICSO) [16] with genetic algorithm optimization with Chi-square similarity measure to cluster a data set then use a self-organizing map to enhance the clustering process of the same data set, both techniques will be compared in terms of accuracy. The evaluation showed that using K-means with ICSO and genetic algorithm achieved the highest average accuracy.

Self-Organizing Map with Generating and Moving Neurons in Visible Space

2007 ◽  
Vol 11 (6) ◽  
pp. 626-632 ◽  
Author(s):  
Kanta Tachibana ◽  
◽  
Takeshi Furuhashi
Keyword(s):  
Large Scale ◽  
Voronoi Tessellation ◽  
Feature Space ◽  
Reference Points ◽  
Self Organizing Map ◽  
Computation Cost ◽  
Mapping Parameters ◽  
Som Algorithm ◽  
Set Up ◽  
Self Organizing

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 combinations of parameters. It is wasteful, however, to set up every possible combination of parameters and to repeatedly run the algorithm from the beginning because the computation cost for learning is large, especially for a large-scale dataset. These problems arise due to the fixing of two types of mapping parameters, i.e., the number and visible positions of neurons. The high computation cost is mainly in the calculation of distances from each sample to all reference points. At the beginning of learning, reference points should be adjusted globally to preserve the topology well because they are initially set far from optimal positions in feature space, e.g. randomly. Such many reference points subdivides feature space into unnecessarily fine Voronoi regions. To avoid this computational waste, it is natural to start learning with a small number of neurons and increase the number of neurons during learning. We propose a new SOM method that varies the number and visible positions of neurons, and thus is applicable also to visible torus and sphere spaces. We apply our proposal to spherical visible space. We use central Voronoi tessellation to move visible positions for two reasons: to tessellate visible space evenly for easy visualization and to level the number of neighboring neurons and better preserve topology. We demonstrate the effect of generating neurons to reduce computation cost and of moving visible positions in visualization and topology preservation.

scholarly journals Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion

2020 ◽  
Vol 2 (2) ◽  
pp. 156-169
Author(s):  
Dhan Lord B. Fortela ◽  
Matthew Crawford ◽  
Alyssa DeLattre ◽  
Spencer Kowalski ◽  
Mary Lissard ◽  
...  
Keyword(s):  
Process Simulation ◽  
Simulation Software ◽  
Self Organizing Map ◽  
Process Variables ◽  
Self Organizing Maps ◽  
Feed Composition ◽  
Syngas Combustion ◽  
Som Algorithm ◽  
Reactor Temperature ◽  
Self Organizing

This study focused on demonstrating the use of a self-organizing map (SOM) algorithm to elucidate patterns among variables in simulated syngas combustion. The work was implemented in two stages: (1) modelling and simulation of syngas combustion under various feed composition and reactor temperature implemented in AspenPlusTM chemical process simulation software, and (2) pattern recognition among variables using SOM algorithm implemented in MATLAB. The varied levels of feed syngas composition and reactor temperature was randomly sampled from uniform distributions using the Morris screening technique creating four thousand eight hundred simulation conditions implemented in the process simulation which consequently produced a multivariate dataset used in the SOM analysis. Results show that cylindrical SOM topology models the dataset at lower quantization error and topographic error as compared to the rectangular SOM topology indicating suitability of the former for variables pattern elucidation for the simulated combustion. Nonetheless, the variables pattern between component planes from rectangular SOM (9 × 28 grid) and those from cylindrical SOM (9 × 28 grid) are almost similar, indicating that either rectangular or cylindrical architectures may be used for variables pattern analysis. The component planes of process variables from trained SOM are a convenient visualization of the trends across all process variables.

scholarly journals Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s Self-Organizing Map

2006 ◽  
Vol 3 (4) ◽  
pp. 1487-1516 ◽  
Author(s):  
L. Peeters ◽  
F. Bação ◽  
V. Lobo ◽  
A. Dassargues
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Three Dimensional ◽  
Spatial Knowledge ◽  
Self Organizing Map ◽  
Data Set ◽  
Hydrochemical Data ◽  
Som Algorithm ◽  
Exploratory Data ◽  
Self Organizing

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 collected in two detritic, phreatic, Cenozoic aquifers in Central Belgium. The standard SOM proves to be more adequate in representing the structure of the data set and to explore relationships between variables. The GEO3DSOM on the other hand performs better in creating spatially coherent groups based on the data.

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