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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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DCC '02 Proceedings of the Data Compression Conference
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Evaluation of Sampling for Data Mining of Association Rules
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IEEE Transactions on Knowledge and Data Engineering
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ACM Transactions on Mathematical Software (TOMS)
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Data & Knowledge Engineering
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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Journal of Computer Security
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This paper presents an efficient framework for error-bounded compression of high-dimensional discrete attributed datasets. Such datasets, which frequently arise in a wide variety of applications, pose some of the most significant challenges in data analysis. Subsampling and compression are two key technologies for analyzing these datasets. PROXIMUS provides a technique for reducing large datasets into a much smaller set of representative patterns, on which traditional (expensive) analysis algorithms can be applied with minimal loss of accuracy. We show desirable properties of PROXIMUS in terms of runtime, scalability to large datasets, and performance in terms of capability to represent data in a compact form. We also demonstrate applications of PROXIMUS in association rule mining. In doing so, we establish PROXIMUS as a tool for preprocessing data before applying computationally expensive algorithms or as a tool for directly extracting correlated patterns. Our experimental results show that use of the compressed data for association rule mining provides excellent precision and recall values (near 100%) across a range of support thresholds while reducing the time required for association rule mining drastically.