Self-Organizing Maps (SOM) are considered by many to be black boxes because the results are often non-intuitive. Our research takes the multidimensional output from a successful intrusion detecting SOM and displays it in novel full color and 3D formats, with landscape features similar to an island, that assist in understanding the SOM results. This paper describes the visual data mining from th...

In this paper, we describe the formatting guidelines for IJCA Journal Submission. The SOM, for kohonen Self Organizing Map, has proven to be a classifier of high caliber in the field of speech recognition signals breasts. Thus, several versions and enhancements were applied on this tool such as recurrent SOM 'RSOM', the growing recurrent SOM 'GRSOM' and the growing hierarchi...

This paper presents a comprehensive, corpus-based study of adjectival concepts. An adjectival concept is a semantic class of adjectives, for example, “adjectives which express feeling”. We represent adjectival concepts by abstract nouns (such as “feeling”), and extract instances where abstract nouns are modified by adjectives (such as “happy feeling”) from a large corpus of Japanese newspaper a...

The Self Organizing Maps (SOM) is regarded as an excellent computational tool that can be used in data mining and data exploration processes. The SOM usually create a set of prototype vectors representing the data set and carries out a topology preserving projection from high-dimensional input space onto a low-dimensional grid such as two-dimensional (2D) regular grid or 2D map. The 2D-SOM tech...

The Growing Self Organizing Map (GSOM) is a dynamic variant of the Self Organizing Map (SOM). It has been mainly used on low dimensional data sets. In this paper the GSOM is applied on high dimensional data sets and its performance is evaluated. Several modifications to the original GSOM algorithm are presented that enable the GSOM to be applied on high dimensional data .The modified version of...

The self-organizing map (SOM), a biologically inspired, learning algorithm from the field of artificial neural networks, is presented as a self-organized critical (SOC) model of the extremal dynamics family. The SOM's ability to converge to an ordered configuration, independent of the initial state, is known and has been demonstrated, in the one-dimensional case. In this ordered configuration i...

Coupled ocean-atmosphere science steadily advances with increasing information obtained from long-records of in situ observations, multiple-year archives of remotely sensed satellite images, and long time series of numerical model outputs. However, the percentage of data actually used tends to be low, in part because of a lack of efficient and effective analysis tools. For instance, it is estim...

The simultaneous treatment of two interrelated and well-known tasks from strategic marketing planning, namely the determination of competitive market structure (CMS) and market segmentation, is addressed via application of the ”Self-Organizing (Feature) Map” (SOM) methodology, as originally proposed by Kohonen (1982). In the present paper, some major aspects of the methodological basis of the S...

The aim of this work is to design a hierarchical model which represents a multi-layer extension of Self-Organizing Map (SOM) variant. The purpose of the proposed system is to create autonomous systems that can learn independently and cooperate to provide a better decision of the phoneme classification. The basic SOM variant is a hybrid model of SOM and Genetic Algorithm (GA) using a growing inc...

The self-organizing map (SOM) has been widely used as a software tool for visualization of high-dimensional data. Important SOM features include information compression while trying to preserve topological and metric relationship of the primary data items. The assumption of topological preservation in SOMs is not true for many data mappings involving dimension reduction. With the automation of ...