| Summary: | Intelligent data analysis technique is an important tool to determine a useful pattern of large dataset. The huge size of multimedia data makes the data management process becomes more complicated.The problem becomes more difficult should multimedia databases are located in different location and would be accessed by the distributed users around the world. It is essential to consider a systematic classification process to improve the performance of multimedia data retrieval and organize. Therefore, the abjectives of this thesis is to apply rough set theory for classifying and analyzing multimedia data and implemented the proposed model under web services environment. To support the study, the methodology of Knowledge Data Discovery is used to extract the hidden pattern in real-world database and then to transform the pattern into undertandable knowledge. Rough set theory is used to represent the imprecision and uncertainty information in a multimedia dataset. The rough set theory elements such as indiscernibility relation, lower and upper approximations, and reduct set are applied for classifying the ambiguity dataset. These elements are also used for evaluating the degree of data accuracy from the database. The rough set theory based toolkit known as ROSETTA is utilized to support the classification process. The tool provides the best method of discretization, reduction, classifier, and split factor throughout the experiments. For the purposes of collaborative environment, the proposed model is implemented under web services platform. The Web services platform could support the communication process of multi-environment users and different types of database. The results of the study show that the proposed classifier model has a better performance compared to other classifier which the highest percentage of accuracy achieved is 99.50.
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