SELF-ORGANIZING MAPS FOR INTERACTIVE INFORMATION GRANULATION

2009

2003

The use of self-organising maps (SOM) in unsupervised knowledge discovery has been successful and widely accepted, since the results produced are unbiased and can be visualised. Growing SOM (GSOM), or dynamic SOM that dynamically allocates map size and shape, was proposed to compensate for the static nature of Kohonen's SOM. GSOM has proven in experiments to decrease the time required to produce a feature map that is of appropriate size for the given data. However, although GSOM usually arrives at similar quantisation error when compared to SOM, it produces considerably higher topographic error. This property has significant influence on the quality of data visualisation and clustering using GSOM, therefore the authors propose an algorithm to enhance topographic quality of GSOM by means of recursive mean directed growing (RMDG) in the growing phase of GSOM while maintaining or even improving its quantisation quality. Furthermore, the authors introduce a dynamic SOM tree model, or hierarchical GSOM, to identify clusters with better accuracy and to visualise cluster separation and merging. Results show improvement of topography preservation when compared to GSOM, and SOM that has similar map size but is not of topologically optimum map aspect ratio. The dynamic SOM tree model demonstrates the ability to allow users to identify clusters interactively and at the same time understand how a larger cluster breaks up into smaller clusters (if it has any) and/or smaller clusters group to form a larger cluster.

Neural networks : the official journal of the International Neural Network Society, 2018

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in b...

Self-Organizing Maps (SOM) are popular unsupervised artificial neural network used to reduce dimensions and visualize data. Visual interpretation from Self-Organizing Maps (SOM) has been limited due to grid approach of data representation, which makes inter-scenario analysis impossible. The paper proposes a new way to structure SOM. This model reconstructs SOM to show strength between variables as the threads of a cobweb and illuminate inter-scenario analysis. While Radar Graphs are very crude representation of spider web, this model uses more lively and realistic cobweb representation to take into account the difference in strength and length of threads. This model allows for visualization of highly unstructured dataset with large number of dimensions, common in Bigdata sources.

Proc. of the 15th Workshop on Self- …, 2005

The Self-Organizing Map (SOM) enjoys significant popularity in the field of data mining and visualization. While its topology-preserving mapping allows easier interpretation of complex data, communicating the location of clusters and individual data items as well as memorizing locations are not solved satisfactorily in conventional rectangular maps. In this paper, a variant of self-organizing maps following standard SOM training practices and having a regular grid structure, but in non-rectangular map shapes, is introduced. Utilizing different recognizable map shapes, such as country or continent maps, or geometrical shapes such as icons, easy description of the location of certain data items becomes possible, and provides an additional mnemonic clue for remembering the locations and relationships between clusters.

2006

The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization methods present different aspects of the information learned by the SOM to gain insight and guide segmentation of the data. In this work, we propose a new visualization scheme that represents data topology superimposed on the SOM grid, and we show how it helps in the discovery of data structure.

2002

In previous work we have proposed a modification of the standard learning algorithm for self-organizing maps that iteratively increases the size of the map during the learning process by adding single neurons. The main advantage of this approach is an automatic control of the size and topology of the map, thus avoiding the problem of misclassification because of an imposed size. In this paper we discuss how this algorithm can be used to visualize changes in data collections. We illustrate our approach with some examples.

2010

The Self-Organizing Map (SOM) is an artificial neural network that is very effective for clustering via visualization. Ideally, so as produce a good model, the output space dimension of the SOM should match the intrinsic dimension of the data. However, because it is very difficult or even impossible to visualize SOM’s with more than two dimensions, the vast majority of applications use SOM with a regular two-dimensional (2D) grid of nodes. For complex problems, this poses a limitation on the quality of the results obtained. There are no theoretical problems in generating SOMs with higher dimensional output spaces, but the 3D SOMs have met limited success. In this paper we show that the 3D SOM can be used successfully for visualizing clusters in georeferenced data. To overcome the problem of visualizing the 3D grid of units, we start by assigning one primary color (of the RGB color scheme) to each of the three dimensions of the 3D SOM. We then use those colors when representing, on a...

2010

The Self-Organizing Map (SOM) is an artificial neural network that performs simultaneously vector quantization and vector projection. Due to this characteristic, the SOM is an effective method for clustering analysis via visualization. The SOM can be visualized through the ...

Information Sciences, 2004

The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capable of projecting high-dimensional data onto a regular, usually 2-dimensional grid of neurons with good neighborhood preservation between two spaces. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to preserve better topology between the two spaces. Besides the neighborhood relationship, our ESOM can detect and preserve an ordering relationship using an expanding mechanism. The computation complexity of the ESOM is comparable with that of the SOM. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM, especially, in terms of the topological error. Furthermore, clustering results generated by the ESOM are more accurate than those obtained by the SOM.