Abstract. Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes the database. This is done using a structure called CM-tree. In order to test our method, the KModes and Click clustering algorithms were used with several databases. Experiments demonstrate that the proposed summarization method improves execution time, without losing clustering quality.
Abstract. This paper deals about the development of new methods and tools to support the management of projects dealing with the design of innovative systems. Currently, there are only few methods and virtually no tool making possible to ensure in a formal way of the design choices coherence with those of project management. This involves risks of incoherencies which can have technical and economical consequences (low quality, wrong deadlines, higher costs...). It is to fill this gap that we currently develop tools, in collaboration with academics and industrials partners, based on a specific representation integrating both technological and organizational data, which will help the designers and the project leader in their respective tasks. In this paper, we present a methodology which, thanks to the interaction of various tools, will lead to establish this project representation and assist the actors of the project in their choices of design and management and so reduces project costs, delays and risks.
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