scholarly journals Application of the self-organising map to trajectory classification

2002 ◽  
Author(s):  
J. Owens ◽  
A. Hunter
Keyword(s):  
The Self ◽  
Trajectory Classification ◽  
Self Organising Map

scholarly journals Using the Self-Organising Map to Identify Regularities across Country-Specific Housing-Market Contexts

10.1068/b3186 ◽  
2005 ◽  
Vol 32 (1) ◽  
pp. 89-110 ◽  
Author(s):  
Tom Kauko
Keyword(s):  
Housing Market ◽  
Semiparametric Regression ◽  
The Self ◽  
Cross Sectional ◽  
Public And Private ◽  
Institutional Differences ◽  
Market Fundamentals ◽  
Self Organising Map ◽  
Public And Private Sector ◽  
Housing Submarkets

The aim of exploring and monitoring housing-market fundamentals (prices, dwelling features, area density, residents, and so on) on a macrolocational level relates to both public and private sector policymaking. Housing market segmentation (that is, the emergence of housing submarkets), a concept with increasing relevance, is defined as the differentiation of housing in terms of the income and preferences of the residents and in terms of administrative circumstances. In order to capture such segmentation empirically, the author applies a fairly new and emerging technique known as the ‘self-organising’ map (SOM), or ‘Kohonen map’. The SOM is a type of (artificial) neural network—a nonlinear and flexible (that is, nonparametric or semiparametric) regression and ‘machine learning’ technique. By utilising the ability of the SOM to visualise patterns, one can analyse various dimensions within the variation of the dataset. Segmentation may then be detected depending on the resulting patterns across the map layers, each of which represents the data variation for one input variable. Utilising an inductive modelling strategy, the author runs cross-sectional and nationwide data on the owner-occupied housing markets of Finland (documentation presented elsewhere), the Netherlands, and Hungary with the SOM technique. On the basis of the resulting configurations certain regularities (similarities and differences) across the three national contexts are identified. In all three cases the segments are determined by physical and institutional differences between the housing bundles and localities. The exercise demonstrates how the inductive SOM-based approach is well-suited for illustrating the contextual factors that determine housing market structure.

A Novel Classified Self-Organising Map Applied to Task Assignment

2020 ◽  
Vol 73 (5) ◽  
pp. 1129-1145
Author(s):  
Yun Qu ◽  
Daqi Zhu
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Task Assignment ◽  
Underwater Vehicles ◽  
The Self ◽  
Sensor Nodes ◽  
Sensor Technology ◽  
The Novel ◽  
Total Energy Consumption ◽  
Map Algorithm ◽  
Self Organising Map

With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given.

A new approach to gene prediction, based on the self-organising map

2010 ◽  
Vol 2 (2) ◽  
pp. 107
Author(s):  
Veera Talukdar ◽  
Amit Konar ◽  
Shibram Paria ◽  
Subhasis Maity ◽  
Tapas Kumar Pal
Keyword(s):  
Gene Prediction ◽  
The Self ◽  
New Approach ◽  
Self Organising Map

scholarly journals Implementing Self Organising Map to Organise the Unstructured Data

2021 ◽  
Vol 2129 (1) ◽  
pp. 012046
Author(s):  
S P Lim ◽  
C K Lee ◽  
J S Tan ◽  
S C Lim ◽  
C C You
Keyword(s):  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Closed Surface ◽  
The Self ◽  
Unstructured Data ◽  
Data Sets ◽  
Minimum Error ◽  
Connectivity Information ◽  
Different Types ◽  
Self Organising Map

Abstract Surface reconstruction is significant in reverse engineering because it should present the correct surface with minimum error using the data available. It has become a challenging process when the data are in the unstructured type and the existing methods are still suffering from accuracy issues. The unstructured data will produce an incorrect surface because there is no connectivity information among the data. So, the unstructured data should undergo the organising process to obtain the correct shape. The Self Organising Map (SOM) has been extensively applied in previous works to solve surface reconstruction problems. However, the performance of the SOM models has remained uncertain. It can be evaluated and tested using different types of data sets. The objectives of this research are to examine the performance and to determine the weaknesses of SOM models. 2D SOM, 3D SOM, and Cube Kohonen (CK) SOM models are investigated and tested using three data sets in this research. As shown in the experimental results, the CKSOM model has proved to perform better because it can represent the correct closed surface with the lowest minimum error.

AHP versus DEA: a comparative analysis for the gradual improvement of unsustainable suppliers

2020 ◽  
Vol 27 (8) ◽  
pp. 2283-2321
Author(s):  
Kamran Rashidi
Keyword(s):  
Comparative Analysis ◽  
Evaluation Method ◽  
Service Providers ◽  
Single Step ◽  
The Self ◽  
Gradual Improvement ◽  
Content Type ◽  
Logistics Service Providers ◽  
Self Organising Map ◽  
High Degree

PurposeData envelopment analysis (DEA) and analytical hierarchy process (AHP) are two widely applied methods to evaluate and rank suppliers in terms of sustainability. In this study, to investigate the extent to which potential differences in the outcomes of these two methods influence the benchmarking strategies, a comparative analysis based on a common set of data gathered from 19 logistics service providers is implemented.Design/methodology/approachAs suppliers' sustainability cannot be improved in a single-step process due to several limitations, improvement needs to proceed gradually. Therefore, using the self-organising map method, the suppliers were classified into clusters within a novel framework for gradually improving their sustainability. Then, the two processes of gradual improvement based on the outcomes of DEA and AHP were compared.FindingsThe findings show that although the rankings of suppliers guided by the methods correlated to a high degree, the benchmarking strategies provided by the methods for gradually improving the sustainability of suppliers differed considerably. In particular, whereas AHP suggests a benchmarking policy better suited for unsustainable or less sustainable suppliers with limited access to resources, DEA proposes one for suppliers able to dramatically boost their sustainability with few quick, significant leaps in performance.Originality/valueFirst, this study revealed a novel gradual improvement framework using the self-organising map method. Second, it clarified the extent to which the benchmarking policies are influenced by the type of evaluation method.

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