Understanding the socio-economic distribution of multiple deprivation: An application of self-organising maps

In this paper we seek to contribute to recent efforts to develop and implement multi-dimensional approaches to social exclusion by applying self-organising maps (SOMs) to a set of material deprivation indicators from the Irish component of EU-SILC. The first stage of our analysis involves the identification of sixteen clusters that confirm the multi-dimensional nature of deprivation in contemporary Ireland and the limitations of focusing solely on income. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of economic stress. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. Most importantly, it demonstrates that conclusions relating to the relative impact of different kinds of socio-economic influences are highly dependent on the form of deprivation being considered. Our analysis suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. (C) 2010 International Sociological Association Research Committee 28 on Social Stratification and Mobility. Published by Elsevier Ltd. All rights reserved.

Mapping patterns of multiple deprivation using self-organising maps: An application to EU-SILC data for Ireland

The development of conceptual frameworks for the analysis of social exclusion has somewhat out-stripped related methodological developments. This paper seeks to contribute to filling this gap through the application of self-organising maps (SOMs) to the analysis of a detailed set of material deprivation indicators relating to the Irish case. The SOM approach allows us to offer a differentiated and interpretable picture of the structure of multiple deprivation in contemporary Ireland. Employing this approach, we identify 16 clusters characterised by distinct profiles across 42 deprivation indicators. Exploratory analyses demonstrate that, controlling for equivalised household income, SOM cluster membership adds substantially to our ability to predict subjective economic stress. Moreover, in comparison with an analogous latent class approach, the SOM analysis offers considerable additional discriminatory power in relation to individuals' experience of their economic circumstances. The results suggest that the SOM approach could prove a valuable addition to a 'methodological platform' for analysing the shape and form of social exclusion. (c) 2009 Elsevier Inc. All rights reserved.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.

The importance of neighbourhood size in self organising systems

In recent times, the analysis of SOM (self-organising map) performance has concentrated on optimising the gain decay, rather than the size, form and decay of the neighbourhood function. We propose that the size, form and decay of region size plays a much more significant role in the learning, and especially in the development, of topographic feature maps. In this paper, a biologically-derived SOM model is presented. This model is able to select a single winning neuron and to form Gaussian outputs about this winner, without the need for a meta-level decision-making structure to artificially select a winner and fit a Gaussian output to that winner. Using this model, some fundamental characteristics of the relationship between neighbourhood size and SOM output states are demonstrated.

An Extension of Self-Organizing Maps to Categorical Data

Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity measure and update method of map evolution for both numeric and categorical features simultaneously.