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In this paper, we employ a novel approach to metarule-guided, multi-dimensional association rule mining which explores a data cube structure. We propose algorithms for metarule-guided mining: given a metarule containing p predicates, we compare mining on an n-dimensional (n-D) cube structure (where p < n) with mining on smaller multiple pdimensional cubes.(More)
A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, clas-siication, prediction, and clustering. By incorporating several interesting data(More)
A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classii-cation, and prediction. By incorporating several interesting data mining techniques, including(More)
Multimedia data mining is the mining of high-level multimedia information and knowledge from large multimedia databases. A multimedia data mining system prototype, MultiMediaMiner, has been designed and developed. It includes the construction of a multimedia data cube which facilitates multiple dimensional analysis of multimedia data, primarily based on(More)
Metarule-guided mining is an interactive approach to data mining, where users probe the data under analysis by specifying hypotheses in the form of metarules, or pattern templates. Previous methods for metarule-guided mining of association rules have primarily used a transac-tion/relation table-based structure. Such approaches require costly, multiple scans(More)
Similarity search on time-series data sets is of growing importance in data mining. With the increasing amount of data of time-series in many applications, from nancial to scientiic, it is important to study the methods of retrieving similarity patterns eeciently and user friendly for business decision making. The thesis proposes methods of eecient(More)
Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting(More)
For many applications such as accounting, banking, business transaction processing systems, geographical information systems, medical record book keeping, etc., the changes made on their databases over time are a valuable source of information which can direct the future operation of the enterprise. In this thesis, we will focus on rela-tional databases(More)
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