How to cite this WIREs title:
WIREs Data Mining Knowl Discov
WIREs Data Mining Knowl Discov
Impact Factor: 4.476
Enhancing Iterative Dichotomiser 3 algorithm for classification decision tree
Focus Article
Published Online: Feb 18 2016
DOI: 10.1002/widm.1177
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Data mining tasks such as clustering and classification have proved to highly impact various fields such as business, including the banking sector, as well as medicine, including the radiology sector. As the decision‐making process is critically dependent on the availability of high‐quality information presented in a timely and easily understood manner, the successful application of efficient data mining approaches is a great support for achieving the required target in the available time. This study presents an enhancement for the Iterative Dichotomiser 3 (ID3) classification decision tree algorithm based on two related approaches, namely, data partitioning and parallelism. The study applied the proposed algorithm in the banking and radiology sectors; as data have been classified to the defined fields’ clusters, the processing time and the results’ accuracy parameters have been compared with the ID3 algorithm and have proved an enhancement in both parameters. WIREs Data Mining Knowl Discov 2016, 6:70–79. doi: 10.1002/widm.1177 This article is categorized under: Algorithmic Development > Hierarchies and Trees Application Areas > Government and Public Sector Application Areas > Health Care Technologies > Classification
Distribution segments of sectors in the testing set of data for eight classifications.
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Distribution segments of investment sectors in the testing set of data for seven classifications.
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Classification trees for the testing set of banking data for seven classifications.
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