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Cluster rule based algorithm for detecting incorrect data records

: El Bekri, Nadia; Peinsipp-Byma, Elisabeth; Syndikus, Andre

Postprint urn:nbn:de:0011-n-4263675 (435 KByte PDF)
MD5 Fingerprint: 9878a5a48e19d27e86d46b67d1be1490
Erstellt am: 20.12.2016

Al-Dabass, David (Ed.) ; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE UKSim-AMSS 2016, 18th International Conference on Modelling & Simulation : Cambridge, United Kingdom 6 - 8 April 2016
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2016
ISBN: 978-1-5090-0887-2
International Conference on Modelling & Simulation (UKSim) <18, 2016, Cambridge>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
data mining; KDD; cluster analysis; Association Rules

Software applications have become an indispensable integral part of this world. In all areas of everyday life they are used to store information. Users of software applications rely on the data correctness. Incorrect data within the data set can cause a reduced user acceptance. To avoid incorrect data sets the process of knowledge discovery in databases (KDD) is a powerful instrument. The application of this process comprises five different steps. The steps are applied successively. One of the core steps is the use of data mining algorithms. This paper outlines the possibilities of combining various data mining algorithms to improve the correctness of the data.