Abstract
Clustering is a part of data mining domain. Its task is to identify groups consisting of similar data objects according to defined similarity criterion. One of the most common problems in this field is the time complexity of algorithms. Reducing the time of processing is particularly important due to constantly growing size of present databases. Granular computing (GrC) techniques create and/or process data portions, called granules, identified with regard to similar description, functionality or behavior. An interesting characteristic of granular computation is the ability to create multi-perspective view of data depending on the resolution level required. Data granules identified on different levels of resolution form a hierarchical structure expressing relations between the objects of data. Granular computing includes methods from various areas with the aim of supporting human in better understanding of analyzed problems and generated results.
The proposed solution of clustering is based on processing granulated data in the form of hyperboxes. The results are compared with the clustering of point-type data with regard to complexity, quality and interpretability.
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Kużelewska, U. (2013). Advantages of Information Granulation in Clustering Algorithms. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2011. Communications in Computer and Information Science, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29966-7_9
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DOI: https://doi.org/10.1007/978-3-642-29966-7_9
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