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Consensus Phase of eDARA

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Development of advanced digital data has been encouraging more scientific data to be stored in relational database for easy access and manipulation. Many approaches have been introduced to extract useful information from the digital data stored in relational database. DARA algorithm is one of the approaches that summarizes data from multi-relation database to produce useful information. As a continuation work of DARA, several Genetic Algorithm (GA) based clustering methods such as Forward Feature Selection, Backward Feature Selection, Bit Merge Feature Construction and Term-term Correlation have been introduced to improve the effectiveness of DARA for data summarization. However, such methods have shown to produce inconsistent performance when dataset from three different domains (mutagenesis, financial and hepatitis) are used for the experiments. Consequently, clustering ensemble is proposed to produce an absolute result of the results that are generated by those methods respectively. In this study, the consensus phase of eDARA that provides a mechanism to combine multiple runs of a clustering algorithm on multi-relation database is presented.
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Keywords: Data Mining; Data Summarization; Ensemble Clustering; One-to-Many Relations; Relational Databases; Vector Space Model

Document Type: Research Article

Affiliations: 1: Faculty of Engineering, Universiti Malasyia Sabah, Kota Kinabalu, Sabah, Malaysia 2: Faculty of Computing and Informatics, Universiti Malasyia Sabah, Kota Kinabalu, Sabah, Malaysia 3: School of Computing, Universiti Utara Malaysia, Changlun, Kedah, Malaysia 4: School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia

Publication date: November 1, 2017

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