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The study is devoted to a granular analysis of data. We develop a new clustering algorithm that organizes findings about data in the form of a collection of information granules – hyperboxes. The clustering carried out here is an example of a granulation mechanism. We discus a compatibility masure guiding a construction (growth) of the clusters and explain a ratioale behind their development. The clustering promotes a data mining way of problem solving by emphasizing the transparency of the results (hyberboxes). We discuss a number of indexes describing hberoxes and expressing relationships between such information granukes. It is also shown how the resulting family of the information granules is a concise descriptor of the structure of the data – a granular signature of the data. We examine the properties of features (variables) occuring of the problem as they manifest in the setting of the information granules. Numerical exeperiments are carried out based on two-dimensional synthetic data as well as multivariable Boston data available on the WWW.
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Projects co-financed by:
Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.
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