Finding structures in vast multidimensional data sets, be they measurement data, statistics, or textual documents, is difficult and time-consuming. Interesting, novel relations between the data items may be hidden in the data. The selforganizing map (SOM) algorithm of Kohonen can be used to aid the exploration: the structures in the data sets can be illustrated on special map displays. In this work, the methodology of using SOMs for exploratory data analysis or data mining is reviewed and developed further. The properties of the maps are compared with the properties of related methods intended for visualizing highdimensional multivariate data sets. In a set of case studies the SOM algorithm is applied to analyzing electroencephalograms, to illustrating structures of the standard of living in the world, and to organizing full-text document collections. Measures are proposed for evaluating the quality of different types of maps in representing a given data set, and for measuring the robu...

The concepts of self-similarity, fractals, and long-range dependence (LRD) have revolutionized network modeling during the last decade. However, despite all the attention these concepts have received, they remain di#cult to use by non-experts. This di#- culty can be attributed to a relative complexity of the mathematical basis, the absence of a systematic approach to their application and the absence of publicly available software. In this paper, we introduce SELFIS, a comprehensive tool, to facilitate the evaluation of LRD by practitioners. Our goal is to create a stand-alone public tool that can become a reference point for the community. Our tool integrates most of the required functionality for an in-depth LRD analysis, including several LRD estimators. In addition, SELFIS includes a powerful approach to stress-test the existence of LRD. Using our tool, evidence are presented that the widely-used LRD estimators can provide misleading results. It is worth mentioning that 25 researchers have acquired SELFIS within a month of its release, which clearly demonstrates the need for such a tool.

Over the last few years, the network community has started to rely heavily on the use of novel concepts such as fractals, self-similarity, long-range dependence, power-laws. Especially evidence of fractals, self-similarity and long-range dependence in network traffic have been widely observed. Despite their wide use, there is still much confusion regarding the identification of such phenomena in real network traffic data. For one, the Hurst exponent can not be calculated in a definitive way, it can only be estimated. Second, there are several different methods to estimate the Hurst exponent, but they often produce conflicting estimates. It is not clear which of the estimators provides the most accurate estimation. In this extended abstract, we make a first step towards a systematic approach in estimating self-similarity and long-range dependence. We present SELFIS, a javabased tool that will automate the self-similarity analysis. To our knowledge, our software tool is the first attempt to create a stand-alone, free, open-source platform to facilitate self-similarity analysis. We show the use of our tool and describe the methodologies that currently incorporates in real Internet data. Finally, we present an intuitive approach to validate the existence of long-range dependence. 1.

: The response time is the most visible performance index to users of computer systems. End-users see individual response times, not the average. Therefore the distribution of response times is important in performance evaluation and capacity planning studies. However, the analytic results cannot be obtained in practical cases. A new method is proposed to approximate the response-time distribution. Unlike the previous methods the proposed one takes into account the service-time distributions and routing behaviour. The reported results indicate that the method provides reasonable approximations in many cases. 1 Introduction Queueing network modelling is a popular tool in the performance evaluation of computer systems. It has been successfully used in various applications of modelling computer systems. In most applications only the mean values of performance indices, such as mean device queue-lengths and the mean system response-time, have been considered. The increasing usage of comput...

Statistical Geometric Features (SGF) have recently been proposed for the classification of image textures. The SGF method is easily extended to use other geometric properties of connected regions. Following a brief review of the method, we propose such an extension to the set of SGF features for the purpose of classifying cervical cell textures. The resulting method proves to be as powerful as the Gray Level Co-occurrence Matrix (GLCM) method of texture analysis, when tested on a set of 117 cervical cell images. The ability to define features tailored to the geometric properties of the textures concerned makes this method a powerful analysis tool. 1. Introduction The extraction of descriptive features from image texture is an important task in image analysis and understanding, and an area of active research interest. Recently, Chen, Nixon & Thomas have proposed a novel set of 16 features for texture classification called "statistical geometric features" (SGF)[1]. This work is of immed...

This report investigates a recently published method of texture analysis called Statistical Geometric Features (SGF). The method is firstly reviewed, salient features discussed, and deficiencies in terms of its application to texture analysis are identified. New features more appropriate to cervical cell texture analysis are defined and trialed. Those features showing discriminatory power are further investigated to determine the cytological properties manifesting the discrimination. The method proves to be as powerful as the Gray Level Co-occurrence Matrix (GLCM) method of texture analysis, when tested on a set of 117 cervical cell images. Moreover, the features defined in this report provide a far better understanding of textural changes within the cell nucleus upon dysplasia, than GLCM features. STATISTICAL GEOMETRIC FEATURES - REFINEMENTS FOR CYTOLOGICAL TEXTURE ANALYSIS

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