the paper introduces uncontrolled fuzzy document clustering and fast fuzzy classification. This system is based on KMART neural network that realizes clustering, and original Fuzzy classification algorithm on the base of Fuzzy ART network that realizes classification. Both algorithms share their weights. Uncontrolled system has two separate flows: by first one we influence structure of categories (plasticity) and second one classifies without possibility to influence defined structure (stability). The paper shows legitimacy of such an approach with regard on quality and speed of classification.I.

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The paper shows the results of experiments focused on the analysis of sustainability of a quality work team in an IT organization. The analysis has been carried out based on the evaluation of the new real dates. Data consists of competences and direction of the strategy of individual specialists, as well as the strategy of the IT team as a whole. The principles of the SWOT analysis and of confrontational matrix have been used to evaluate the strategy and focus of individuals. The next step of the solution assumes the use of the principles of artificial intelligence in the process of solving relations within organizations.

This paper is oriented into the text document retrieval area. The aim of the paper is to compare two soft document clustering methods by using neural networks, the modification of KMART and the nonlinear Hebbian neural network with Oja learning rule.

This article describes the design of a new model IKMART, for classification of documents and their incorporation into categories based on the KMART architecture. The architecture consists of two networks that mutually cooperate through the interconnection of weights and the output matrix of the coded documents. The architecture retains required network features such as incremental learning without the need of descriptive and input/output fuzzy data, learning acceleration and classification of documents and a minimal number of user-defined parameters. The conducted experiments with real documents showed a more precise categorization of documents and higher classification performance in comparison to the classic KMART algorithm.