Evaluation of Clusters Quality in Artificial Immune Clustering System - SArIS

Wierzchoń, Sławomir T.; Kużelewska, Urszula

doi:10.1007/978-0-387-36503-9_30

Cited by 2 publications

(2 citation statements)

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“…The granulation is performed by clustering and the clusters can be identified on the different level of resolution. The prototype of the algorithm is a method described in (Wierzchoń and Kużelewska, 2006). However, in SOSIG granulation property and application to cope with different attributes types was introduced.…”

Section: Self-organizing System For Information Granulationmentioning

confidence: 99%

“…This follows fundamental changes in its implementation. In the following description of the algorithm there are used new terms and symbols in contrary to the description from (Wierzchoń and Kużelewska, 2006) more compatible with GrC theory. SOSIG creates a network structure of connected objects (in general points, but in the presented solution hyperboxes) forming clusters.…”

Section: Self-organizing System For Information Granulationmentioning

confidence: 99%

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Clustering With Granular Information Processing

Kużelewska¹

2011

Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

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Clustering is a part of data mining domain. Its task is to detect groups of similar objects on the basis of established similarity criterion. Granular computing (GrC) includes methods from various areas with the aim to support human with better understanding analyzed problem and generated results. Granular computing techniques create and/or process data portions named as granules identified with regard to similar description, functionality or behavior. Interesting characteristic of granular computation is offer of multi-perspective view of data depending on required resolution level. Data granules identified on different levels of resolution form a hierarchical structure expressing relations between objects of data. A method proposed in this article performs creation data granules by clustering data in form of hyperboxes. The results are compared with clustering of point-type data with regard to complexity, quality and interpretability.

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Section: Self-organizing System For Information Granulationmentioning

confidence: 99%

Section: Self-organizing System For Information Granulationmentioning

confidence: 99%