Data mining using rule extraction from Kohonen self-organising maps
| Autor: | Kenneth McGarry, Chris Bowerman, James Malone, Stefan Wermter |
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| Rok vydání: | 2005 |
| Předmět: |
Self-organizing map
sub_databases Artificial neural network Computer science computer.software_genre sub_informationsystems Exploratory data analysis ComputingMethodologies_PATTERNRECOGNITION Models of neural computation Knowledge extraction Artificial Intelligence Key (cryptography) Feature (machine learning) top_computing Data mining Cluster analysis computer Software |
| Zdroj: | Neural Computing and Applications. 15:9-17 |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-005-0002-1 |
| Popis: | The Kohonen self-organising feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two-dimensional form. This feature is used for classification and clustering of data. However, a great deal of effort is still required to interpret the cluster boundaries. In this paper we present a technique which can be used to extract propositional IF..THEN type rules from the SOM network’s internal parameters. Such extracted rules can provide a human understandable description of the discovered clusters. |
| Databáze: | OpenAIRE |
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