Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SO...

The peptide substances P (SP) and somatostatin (SOM) are present in small-diameter neurones of dorsal root ganglia (DRG) and in small-diameter fibres that project to the spinal cord dorsal horn. It is not known whether SP or SOM coexist with other transmitter molecules but, since both peptides can be released from sensory neurones and both can alter neuronal firing rates in the dorsal horn, it ...

Objective(s):This study examined whether conjugated linoleic acid (CLA) supplementation affects insulin sensitivity and peroxisome proliferator-activated receptor gamma (PPAR-γ) and glucose transporter type 4 (GLUT-4) protein expressions in the skeletal muscles of rats during endurance exercise. Materials and Methods:Sprague-Dawley male rats were randomly divided into HS (high-fat diet (HFD) s...

The aim of this article is to inquire about correlations between criminal phenomena and demographic factors. This international-level comparative study used a dataset covering 56 countries and 28 attributes. The data were processed with the Self-Organizing Map (SOM), assisted other clustering methods, and several statistical methods for obtaining comparable results. The article is an explorator...

In this paper, we discuss the use of Self Organizing Maps (SOM) for character and word clustering. The SOM is a particular kind of artificial neural network that computes an unsupervised clustering of the input data arranging the cluster centers in a lattice. After an overview of the previous applications of unsupervised learning and SOM in the field of Document Image Analysis we describe our r...

SOM is a type of unsupervised learning where the goal is to discover some underlying structure of the data. In this paper, a new extraction method based on the main idea of Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks is proposed. Each SOM of the system is trained individually to provide best results for one class only. The experiments...

Clustering algorithms generally suffer from some well-known problems for which the Self Organizing Maps (SOM) algorithms are adept at handling. While there are many variants of the SOM algorithm, software programmes that implement the SOM algorithms have tended to give varying results even when tested on the same data sets. This can have serious implications when the goal of the clustering is n...

This paper presents a novel retrieval method for effective search of palmprints based on Principal Component Analysis (PCA) and Self-Organizing Feature Map (SOM). To reduce search space and speed up the query processing, an integration of PCA and SOM is proposed, where the coefficients obtained by PCA for global feature representation is considered as input features of SOM. The trained SOM can ...

In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are imp...