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WIREs Data Mining Knowl Discov
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Enhancing Iterative Dichotomiser 3 algorithm for classification decision tree

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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

Resources and usage of cash in bank.
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Enhanced_ID3 classification for cluster.
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ID3 classification for cluster.
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Enhanced_ID3 classification for .
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ID3 classification for cluster.
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Distribution segments of sectors in the testing set of data for eight classifications.
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Classification for radiology data.
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Enhanced_ID3 classification for natural disaster risks cluster.
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ID3 classification for operational risks cluster.
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Enhanced_ID3 classification for trading cluster.
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ID3 classification for agricultural cluster.
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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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Investment methods.
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Browse by Topic

Algorithmic Development > Hierarchies and Trees
Application Areas > Government and Public Sector
Application Areas > Health Care
Technologies > Classification

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