This paper presents a technique in classifying the images into a number of classes or clusters desired by means of Self Organizing Map (SOM) Artificial Neural Network method. A number of 250 color images to be classified as previously done some processing, such as RGB to grayscale color conversion, color histogram, feature vector selection, and then classifying by the SOM Feature vector selecti...

Bankruptcy trajectory reflects the dynamic changes of financial situation of companies, and hence make possible to keep track of the evolution of companies and recognize the important trajectory patterns. This study aims at a compact visualization of the complex temporal behaviors in financial statements. We use self-organizing map (SOM) to analyze and visualize the financial situation of compa...

We propose a novel scheme for designing fuzzy rule based classifier. An SOFM based method is used for generating a set of prototypes which is used to generate a set of fuzzy rules. Each rule represents a region in the feature space that we call the context of the rule. The rules are tuned with respect to their context. We justified that the reasoning scheme may be different in different context...

In this work, we present two methods for analyzing the syllables of the bird song on the Self-Organizing Map (SOM). Dynamic time warping is used for computing the distances between the data sequences. In the first method, the pairwise distances are first computed between the data sequences and each row of the distance matrix is then considered as a feature vector. The conventional SOM with fixe...

In the last years, the improvements in Magnetic Resonance Imaging systems (MRI) provide new and additional ways to diagnose some brain disorders such as schizophrenia or the Alzheimer disease. One way to figure out these disorders from a MRI is through image segmentation. Image segmentation consist in partitioning an image into different regions. These regions determine different tissues presen...

This paper proposes a generation method of a subject-specific Facial Expression Map (FEMap) using the Self-Organizing Maps (SOM) of unsupervised learning and Counter Propagation Networks (CPN) of supervised learning together. The proposed method consists of two steps. In the first step, the topological change of a face pattern in the expressional process of facial expression is learned hierarch...

The Self-Organizing Map (SOM) is very often visualized by applying Ultsch’s Unified Distance Matrix (UMatrix) shading and labeling the cells of the 2-D grid with training data observations nearest to that node in feature space. Although powerful and the de facto standard visualization for SOMs, this does not provide for two key pieces of information when considering real world data mining appli...

Self Organizing Maps are efficient and usual for dimension reduction and data clustering. In our present work, we propose the use of Kohonen Topologic Map for fingerprint pattern classification. The learning process takes into account the large intra-class diversity and the continuum of fingerprint pattern types. After a brief introduction to fingerprint domain-specific knowledge and the expert...

information from multidimensional primary signals, and to represent it as a location, say, in a two-dimensional network. Although this i s already a step towards generalization and symbolism, it must be admitted that the extraction of features from geometrically or physically relatable data elements i s still a very concrete task, in principle at least. Theoperation of the brain at the higher l...

We present a study of a novel variant of the Self-Organizing Map (SOM) called the Associative SelfOrganizing Map (A-SOM). The A-SOM is similar to the SOM and thus develops a representation of its input space, but in addition it also learns to associate its activity with the activity of one or several external SOMs. The A-SOM has relevance in e.g. the modelling of expectations in one modality du...