Ari Visa

Engineering applications of the self-organizing map

The self-organizing map (SOM) method is a new, powerful software tool for the visualization of high-dimensional data. It converts complex, nonlinear statistical relationships between high-dimensional data into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data elements on the display, it may also be thought to produce some kind of abstractions. The term self-organizing map signifies a class of mappings defined by error-theoretic considerations. In practice they result in certain unsupervised, competitive learning processes, computed by simple-looking SOM algorithms. Many industries have found the SOM-based software tools useful. The most important property of the SOM, orderliness of the input-output mapping, can be utilized for many tasks: reduction of the amount of training data, speeding up learning nonlinear interpolation and extrapolation, generalization, and effective compression of information for its transmission.

Combining Data and Text Mining Techniques for Analysing Financial Reports

There is a vast amount of financial information on companies’ financial performance available to investors today. While automatic analysis of financial figures is common, it has been difficult to automatically extract meaning from the textual part of financial reports. The textual part of an annual report contains richer information than the financial ratios. In this paper, we combine data mining methods for analyzing quantitative and qualitative data from financial reports, in order to see if the textual part of the report contains some indication about future financial performance. The quantitative analysis has been performed using selforganizing maps, and the qualitative analysis using prototype-matching text clustering. The analysis is performed on the quarterly reports of three leading companies in the telecommunications sector.

Shape recognition of irregular objects

A new approach to object recognition is proposed. The main concern is on irregular objects which are hard to recognize even for a human. The recognition is based on the contour of an object. The contour is obtained with morphological operators and described with a Freeman chain code. The chain code histogram (CCH) is calculated from the chain code of the contour of an object. For an eight-connected chain code an eight dimensional histogram, which shows the probability of each direction, is obtained. The CCH is a translation and scale invariant shape measure. The CCH gibes only an approximation of the objects shape so that similar objects can be grouped together. The discriminatory power of the CCH is demonstrated on machine-printed text and on true irregular objects. In both cases it is noted that similar objects are grouped together. The results of experiments are good. It has been shown that similar objects are grouped together with the proposed method. However, the sensitivity to small rotations limits the generality of the method.

Multiscale Fourier descriptors for defect image retrieval

Abstract Shape is an essential visual feature of an image and it is widely used to describe image content in image classification and retrieval. In this paper, two new Fourier-based approaches for contour-based shape description are presented. These approaches present Fourier descriptors in multiple scales, which improves the shape classification and retrieval accuracy. The proposed methods outperform ordinary Fourier descriptors in the retrieval of complicated shapes without increasing computational cost.

Multiscale Fourier descriptor for shape-based image retrieval

The shapes occurring in the images are important in the content-based image retrieval. We introduce a new Fourier-based descriptor for the characterization of the shapes for retrieval purposes. This descriptor combines the benefits of the wavelet transform and Fourier transform. This way the Fourier descriptors can be presented in multiple scales, which improves the shape retrieval accuracy of the commonly used Fourier-descriptors. The multiscale Fourier descriptor is formed by applying the complex wavelet transforms to the boundary function of an object extracted from an image. After that, the Fourier transform is applied to the wavelet coefficients in multiple scales. This way the multiscale shape representation can be expressed in a rotation invariant form. The retrieval efficiency of this multiscale Fourier descriptor is compared to an ordinary Fourier descriptor and CSS-shape representation.

Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking:

In this paper, we illustrate the use of the self-organizing map technique for financial performance analysis and benchmarking. We build a database of financial ratios indicating the performance of 91 international pulp and paper companies for the time period 1995–2001. We then use the self-organizing map technique to analyze and benchmark the performance of the five largest pulp and paper companies in the world. The results of the study indicate that by using the self-organizing maps, we are able to structure, analyze, and visualize large amounts of multidimensional financial data in a meaningful manner.

Comparing numerical data and text information from annual reports using self-organizing maps

Abstract More and more companies provide their accounting information in electronic form today. The accounting information in electronic form can be found in large commercial databases or on the web. This information is of great interest for different stakeholders, i.e., stockholders, creditors, auditors, financial analysts, and management. For the stakeholders it is important to be able to extract both quantitative and qualitative information concerning the companies they are interested in. The annual reports contain information both in numerical and symbolic form. So far, only the numerical information has been analyzed with help of computers. However, technology has evolved and in particular neural networks in the form of self-organizing maps (SOMs) provide a new tool for analyzing also text information. In this paper, we compare results on quantitative data with results on qualitative data from annual reports. We use smart encoding, SOMs, and document histograms for comparing the performance of forest companies worldwide. Firstly, we cluster the companies according to, on the one hand, quantitative information, and on the other hand, qualitative information. Secondly, we compare the results produced by the clustering methods. Our results of the comparison show that there is a difference between the results.

The language of quarterly reports as an indicator of change in the company's financial status

This paper adopts a multi-methodological approach to information systems research in order to produce new information through data mining. This approach is particularly suitable for mining material that consists of both qualitative and quantitative information. The contents of quarterly reports from three telecommunications companies were compared. The study focused on the years 2000-2001, a period of economic decline for many IT companies. The central quantitative data, reflected by seven financial ratios, were visualised using self-organising maps. The qualitative data, consisting of the textual contents of the reports, were visualised using collocational networks; these showed the relationships between the central concepts in the texts. As the visualisations of the contents were compared, certain patterns could be found. The results seemed to suggest that changes in the networks indicated future changes in the self-organising maps. In the cases studied, a change in the textual data usually indicated a change in the financial data in the following quarter. This may be a consequence of the fact that the texts reflected the plans and future expectations of management, whereas the financial ratios reflected the current financial situation of the company.

Multiscale Fourier descriptor for shape classification

The description of object shape is an important characteristic of an image. In image processing and pattern recognition, several different shape descriptors are used. In human visual perception, shapes are processed in multiple resolutions. Therefore, multiscale shape representation is essential in shape based image classification and retrieval. In the description of an object shape, the multiresolution representation provides also additional accuracy to the shape classification. We introduce a new descriptor for shape classification. This descriptor is called the multiscale Fourier descriptor, and it combines the benefits of a Fourier descriptor and multiscale shape representation. This descriptor is formed by applying a Fourier transform to the coefficients of the wavelet transform of the object boundary. In this way, the Fourier descriptor can be presented in multiple resolutions. We performed classification experiments using three image databases. The classification results of our method are compared to those of Fourier descriptors.

An adaptive texture and shape based defect classification

In this paper classification of surface defects is considered. The classification system consists of several classifiers whose outputs are combined in order to produce the final classification. The self-organizing maps (SOMs) are used as classifiers. Each SOM is taught unsupervised with examples of defects. Classification is based on the internal structure and the shape characteristics of defects. Texture features from the co-occurrence matrix and the gray level histogram are used to describe the internal structure. The set of simple shape descriptors is used for shape characterization The results of experiments with base paper defects are encouraging.