Clustering of City Housing Facilities Based on Self-Organizing Maps

2015 ◽  
Vol 725-726 ◽  
pp. 1057-1062 ◽  
Author(s):  
Tatiana Simankina ◽  
Olga Popova
Keyword(s):  
Housing Stock ◽  
Network Modeling ◽  
Self Organization ◽  
Neural Network Modeling ◽  
Self Organizing Maps ◽  
High Level ◽  
The City ◽  
Regional Programs ◽  
Housing Facilities ◽  
Self Organizing

The algorithm for clustering based on neural network modeling using T. Kohonen's self-organizing maps for the analysis of the housing stock is considered. This analysis of housing stock is required for the planning of complex reproduction of housing and major repairs regional programs development. The mechanism of self-organization is submitted. The representative sample clustering of the housing stock is produced. Its result is 16 groups of objects with a high level of internal similarity. The basic advantages of this approach for monitoring and analysis of the city housing stock are described.

DISCOVERING STOCK TRADING PREFERENCES BY SELF-ORGANIZING MAPS AND DECISION TREES

2009 ◽  
Vol 18 (04) ◽  
pp. 603-611 ◽  
Author(s):  
CHIH-FONG TSAI ◽  
YUAH-CHIAO LIN ◽  
YI-TING WANG
Keyword(s):  
Individual Investors ◽  
Self Organizing Map ◽  
Tree Model ◽  
Stock Trading ◽  
Self Organizing Maps ◽  
Two Factors ◽  
Group A ◽  
Trading Activities ◽  
High Level ◽  
Self Organizing

Stock trading activities are always very popular in many countries. Generally, investors with various backgrounds have different preferences over the stocks they trade. In literature, a number of studies examine the institutions' holding preferences for certain stock characteristics when choosing the security portfolio. However, very few studies investigate the stock trading preferences of individual investors. In this paper, we focus on two factors which affect the portfolio choices of investors, which are stock characteristics and investor features. In particular, a self-organizing map (SOM) is used to group a certain number of clusters based on a chosen dataset. Then, the decision tree model is used to extract useful rules from the clusters which contain the most trading records in the sample. We find that if the investors are females, less wealthy, and make stock trades with lower frequencies, they will be more careful and conservative. On the other hand, if the investors are males, having a high level of wealth, and make stock trades very often, they tend to choose stocks with high EPS, high market-to-book, and high prices.

scholarly journals Complex assessment of urban housing energy sustainability

2018 ◽  
Vol 33 ◽  
pp. 02041 ◽  
Author(s):  
Olga Popova ◽  
Julia Glebova ◽  
Irina Karakozova
Keyword(s):  
Housing Stock ◽  
Residential Development ◽  
Urban Housing ◽  
Technical Condition ◽  
Environmental Benefits ◽  
Self Organizing Maps ◽  
Energy Parameters ◽  
High Rise ◽  
Upward Growth ◽  
The City

The article presents the results of a complex experimental-analytical research of residential development energy parameters - survey of construction sites and determination of calculated energy parameters (resistance to heat transfer) considering their technical condition. The authors suggest a methodology for assessing residential development energy parameters on the basis of construction project’s structural analysis with the use of advanced intelligent collection systems, processing (self-organizing maps - SOM) and data visualization (geo-informational systems - GIS). SOM clustering permitted to divide the housing stock (on the example of Arkhangelsk city) into groups with similar technical-operational and energy parameters. It is also possible to measure energy parameters of construction project of each cluster by comparing them with reference (normative) measures and also with each other. The authors propose mechanisms for increasing the area’s energy stability level by implementing a set of reproduction activities for residential development of various groups. The analysis showed that modern multilevel and high-rise construction buildings have the least heat losses. At present, however, ow-rise wood buildings is the dominant styles of buildings of Arkhangelsk city. Data visualisation on the created heat map showed that such housing stock covers the largest urban area. The development strategies for depressed areas is in a high-rise building, which show the economic, social and environmental benefits of upward growth of the city. An urban regeneration programme for severely rundown urban housing estates is in a high-rise construction building, which show the economic, social and environmental benefits of upward growth of the city.

scholarly journals Una Exploración Conceptual de la Formación de Patrones en Sistemas Sociales Autorganizados

Ingeniería ◽  
2018 ◽  
Vol 23 (1) ◽  
pp. 84
Author(s):  
David Anzola
Keyword(s):  
Pattern Formation ◽  
Interdisciplinary Collaboration ◽  
Complexity Science ◽  
Self Organization ◽  
Social Self ◽  
Social Scientists ◽  
Self Organized ◽  
Scientists And Engineers ◽  
High Level ◽  
Self Organizing

Context: The concept of self-organization plays a major role in contemporary complexity science. Yet, the current framework for the study of self-organization is only able to capture some of the nuances of complex social self-organizing phenomena.Method: This article addresses some of the problematic elements in the study of social selforganization. For this purpose, it focuses on pattern formation, a feature of self-organizing phenomena that is common across definitions. The analysis is carried out through three main questions: where can we find these patterns, what are these patterns and how can we study these patterns.Results: The discussion shows that there is a high level of specificity in social self-organized phenomena that is not adequately addressed by the current complexity framework. It argues that some elements are neglected by this framework because they are relatively exclusive to social science; others, because of the relative novelty of social complexity.Conclusions: It is suggested that interdisciplinary collaboration between social scientists and complexity scientists and engineers is needed, in order to overcome traditional disciplinary limitations in the study of social self-organized phenomena.

scholarly journals Self-Organizing Representation Learning

2021 ◽  
Author(s):  
noureddine kermiche
Keyword(s):  
Data Augmentation ◽  
Representation Learning ◽  
Self Organization ◽  
Learning Methods ◽  
Self Organizing Maps ◽  
Output Layer ◽  
Data Representations ◽  
Augmentation Techniques ◽  
Using Data ◽  
Self Organizing

Using data augmentation techniques, unsupervised representation learning methods extract features from data by training artificial neural networks to recognize that different views of an object are just different instances of the same object. We extend current unsupervised representation learning methods to networks that can self-organize data representations into two-dimensional (2D) maps. The proposed method combines ideas from Kohonen’s original self-organizing maps (SOM) and recent development in unsupervised representation learning. A ResNet backbone with an added 2D <i>Softmax</i> output layer is used to organize the data representations. A new loss function with linear complexity is proposed to enforce SOM requirements of winner-take-all (WTA) and competition between neurons while explicitly avoiding collapse into trivial solutions. We show that enforcing SOM topological neighborhood requirement can be achieved by a fixed radial convolution at the 2D output layer without having to resort to actual radial activation functions which prevented the original SOM algorithm from being extended to nowadays neural network architectures. We demonstrate that when combined with data augmentation techniques, self-organization is a simple emergent property of the 2D output layer because of neighborhood recruitment combined with WTA competition between neurons. The proposed methodology is demonstrated on SVHN and CIFAR10 data sets. The proposed algorithm is the first end-to-end unsupervised learning method that combines data self-organization and visualization as integral parts of unsupervised representation learning.

scholarly journals Document Clustering using Self-Organizing Maps

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 111-118
Author(s):  
Muhammad Rafi ◽  
Muhammad Waqar ◽  
Hareem Ajaz ◽  
Umar Ayub ◽  
Muhammad Danish
Keyword(s):  
Document Clustering ◽  
Feature Space ◽  
Good Method ◽  
Self Organization ◽  
Self Organizing Maps ◽  
Document Collections ◽  
Document Collection ◽  
Common Technique ◽  
Low Dimensional ◽  
Self Organizing

Cluster analysis of textual documents is a common technique for better ltering, navigation, under-standing and comprehension of the large document collection. Document clustering is an autonomous methodthat separate out large heterogeneous document collection into smaller more homogeneous sub-collections calledclusters. Self-organizing maps (SOM) is a type of arti cial neural network (ANN) that can be used to performautonomous self-organization of high dimension feature space into low-dimensional projections called maps. Itis considered a good method to perform clustering as both requires unsupervised processing. In this paper, weproposed a SOM using multi-layer, multi-feature to cluster documents. The paper implements a SOM usingfour layers containing lexical terms, phrases and sequences in bottom layers respectively and combining all atthe top layers. The documents are processed to extract these features to feed the SOM. The internal weightsand interconnections between these layers features(neurons) automatically settle through iterations with a smalllearning rate to discover the actual clusters. We have performed extensive set of experiments on standard textmining datasets like: NEWS20, Reuters and WebKB with evaluation measures F-Measure and Purity. Theevaluation gives encouraging results and outperforms some of the existing approaches. We conclude that SOMwith multi-features (lexical terms, phrases and sequences) and multi-layers can be very e ective in producinghigh quality clusters on large document collections.

scholarly journals Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1605 ◽  
Author(s):  
Lyes Khacef ◽  
Laurent Rodriguez ◽  
Benoît Miramond
Keyword(s):  
Unsupervised Learning ◽  
Cortical Plasticity ◽  
Internal Representation ◽  
Neural System ◽  
Self Organization ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Dynamic Vision ◽  
Neuromorphic Architecture ◽  
Self Organizing

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a Dynamic Vision Sensor (DVS)/EletroMyoGraphy (EMG) hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system’s topology is not fixed by the user but learned along the system’s experience through self-organization.

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