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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15374

Title: Performance Evaluation of RULES-3 Induction System for Data Mining
Authors: Mehmet Sabih AKSOY
Hassan Mathkour
Bader Ali ALASOOS
Keywords: Inductive reasoning, Data Mining, Knowledge Acquisition, Rules3, Machine Learning
Issue Date: 2010
Abstract: Data mining has been recognized as a key research topic in database systems and machine learning. It aims to discover a useful knowledge from large amount of data. Data mining become one of the most important tools used for solving most of today's problems that are related to different sectors of our life. Different techniques have been developed for mining data in statistics, machine learning, and other disciplines. These techniques need to be re-evaluated, and scalable algorithms should be developed for effective data mining. This paper will investigate the use of RULES-3 Inductive Learning Algorithm for data mining by comparing it with three statistical, two Lazy, and six rule-based data mining algorithms on eleven real life data sets in terms of learning rate, accuracy and robustness to noisy and incomplete data.
URI: http://hdl.handle.net/123456789/15374
Appears in Collections:College of Computer and Information Sciences

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