Use of Self Organizing Maps in Technique Analysis

This study looked at the coordination patterns of four participants performing three different basketball shots from different distances. The shots selected were the three-point shot, the free throw shot and the hook shot; the latter was included to encourage a phase transition between shots. We hypothesised lower variability between the three-point and free throw shots compared to the hook shot. The study uses Self-Organizing Maps (SOM) to expose the non-linearity of the movement and to try to explain more specifically what it is about the coordination patterns that make them different or similar. The SOM proved to draw the researcher\u27s attention to aspects of the movement that were not obvious from a visual analysis of the original movement either viewed from video or as computer animation. A speculative link between the observational learning literature on the importance of the kinematics of distal segments in skill acquisition and the visual information a coach or analyst may rely on for qualitative technique analysis was made. Although making the distinction between the three shooting conditions was meant to be a trivial exercise, in many cases for this dataset the SOM output and the natural inclination of the movement analyst did not agree: the SOM may provide a more objective method for explaining movement patterning

GEMA: An open-source Python library for self-organizing-maps

Organizations have realized the importance of data analysis and its benefits. This in combination with Machine Learning algorithms has allowed to solve problems more easily, making these processes less time-consuming. Neural networks are the Machine Learning technique that is recently obtaining very good best results. This paper describes an open-source Python library called GEMA developed to work with a type of neural network model called Self-Organizing-Maps. GEMA is freely available under GNU General Public License at GitHub (https://github.com/ufvceiec/GEMA). The library has been evaluated in different a particular use case obtaining accurate results

Gait Based Vertical Ground Reaction Force Analysis for Parkinson's Disease Diagnosis Using Self Organizing Map

ABSTRACT The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson's disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysis methods and to find new clinical ways for observing the large amount of information obtained in a gait lab. Self organizing maps (SOM) also called Kohonen maps are a special kind of neural networks that can be used for clustering tasks. The results are shown in the terms of sensitivity, specificity, accuracy, error rate from the two groups of features which are the Mean Coefficient of Variation and Mean Sum of Variation and Mean Max and Mean Standard deviation of the Ground Reaction Force. Results showing the potential of this technique for distinguishing between population of individuals with normal gait and with gait disorders of different causes of disease

Feature Extraction Methods by Various Concepts using SOM

Image retrieval systems gained traction with the increased use of visual and media data. It is critical to understand and manage big data, lot of analysis done in image retrieval applications. Given the considerable difficulty involved in handling big data using a traditional approach, there is a demand for its efficient management, particularly regarding accuracy and robustness. To solve these issues, we employ content-based image retrieval (CBIR) methods within both supervised , unsupervised pictures. Self-Organizing Maps (SOM), a competitive unsupervised learning aggregation technique, are applied in our innovative multilevel fusion methodology to extract features that are categorised. The proposed methodology beat state-of-the-art algorithms with 90.3% precision, approximate retrieval precision (ARP) of 0.91, and approximate retrieval recall (ARR) of 0.82 when tested on several benchmark datasets

Behavioral analysis for virtualized network functions: A som-based approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions

Behavioral Analysis for Virtualized Network Functions : A SOM-based Approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions

Clustering biological data with self-adjusting high-dimensional sieve

Data classification as a preprocessing technique is a crucial step in the analysis and understanding of numerical data. Cluster analysis, in particular, provides insight into the inherent patterns found in data which makes the interpretation of any follow-up analyses more meaningful. A clustering algorithm groups together data points according to a predefined similarity criterion. This allows the data set to be broken up into segments which, in turn, gives way for a more targeted statistical analysis. Cluster analysis has applications in numerous fields of study and, as a result, countless algorithms have been developed. However, the quantity of options makes it difficult to find an appropriate algorithm to use. Additionally, the more commonly used algorithms, while precise, require a familiarity with the data structure that may be resource-consuming to attain. Here, we address this concern by developing a novel clustering algorithm, the sieve method, for the preliminary cluster analysis of high-dimensional data. We evaluate its performance by comparing it to three well-known clustering algorithms for numerical data: the k-means, single-linkage hierarchical, and self-organizing maps. To compare the algorithms, we measure accuracy by using the misclassification or error rate of each algorithm. Additionally, we compare the within- and between-cluster variation of each clustering result through multivariate analysis of variance. We use each algorithm to cluster Fisher\u27s Iris Flower data set, which consists of 3 ``true\u27\u27 clusters and 150 total observations, each made up of four numerical measurements. When the optimal clustering structure is known, we found that the k-means and self-organizing maps are the more efficient algorithms in terms of speed and accuracy. When this structure is not known, we found that the sieve algorithm, despite higher misclassification rates, was able to obtain the optimal clustering structure through a truly blind clustering. Thus, the sieving algorithm functions as an informative and blind preliminary clustering method that can then be followed-up by a more refined algorithm. The existence of reliably efficient clustering process for numerical data means that more time, effort, and computational resources can be spent on a more rigorous and targeted statistical analysis

Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions