the breakdown of financial markets into sectors provides an intuitive classification for groups of companies. The allocation of a company to a sector is an expert task, in which the company is classified by the activity that most closely describes the nature of the company's business. Individual share price movement is dependent upon many factors, but there is an expectation for shares within a market sector to move broadly together. We are interested in discovering if share closing prices do move together, and whether groups of shares that do move together are identifiable in terms of industrial activity. Using TreeGNG, a hierarchical clustering algorithm, on a time series of share closing prices, we have identified groups of companies that cluster into clearly identifiable groups. These clusters compare favourably to a globally accepted sector classification scheme, and in our opinion, our method identifies sector structure clearer than a statistical agglomerative hierarchical clustering method.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.---- Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. DOI : 10.1109/ISCIT.2004.1413871Rainfall prediction is a challenging task especially in a modern world facing the major environmental problem of global warming. The proposed method uses an Adaptive Radial Basis Function neural network mode with a specially designed gerietic algoruhm (CA) to obtain the optimal model parameters. A significant feature of the Adaptive Radinl Basis Function network is that it is able creak new hidden units and solve the spread factor problem using a genetic algorithm. It is shown that the evolved parameter values improved performance

This paper describes experiments performed using a Genetic Algorithm (GA) to optimise the parameters of a novel model of a stochastic hierarchical neural clusterer. Two issues of enhancing and optimising the model are discussed. Two fitness functions were created from two selected clustering measures, and a population of genotypes, specifying parameters of the model were evolved. Using the idea of optimising the model by a GA has been proven to be useful. This process mirrors genomic evolution and ontogeny.

This paper describes various mechanisms for adding stochasticity to a dynamic hierarchical neural clusterer. Such a network grows a tree-structured neural classifier dynamically in response to the unlabelled data with which it is presented. Experiments are undertaken to evaluate the effects of this addition of stochasticity. These tests were carried out using two sets of internal parameters, that define the characteristics of the neural clusterer. A Genetic Algorithm using appropriate cluster criterion measures in its fitness function was used to search the parameter space for these instantiations. It was found that the addition of non-determinism produced more reliable clustering performances especially on unseen real world data. Finally, deliberately changing the tree shape by varying key parameters was investigated, illustrated and systematically analysed.

This paper documents experiments performed using a CA to optimise the parameters of a dynamic neural tree model. Two fitness functions were created from two selected clustering measures, and a population of genotypes, specifying parameters of the model were evolved. This process mirrors genomic evolution and ontogeny. It is shown that the evolved parameter values improved performance.