document: PROXIMUS : a framework for analyzing very high dimensional discrete-attributed datasets 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 .
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