X. Hu and N. Cercone. Rough Set Similarity Based Learning from Databases. In Proc. of The first International Conference on Knowledge Discovery and Data Mining, pages 162--167, 1995. Home/Search Document Not in Database Summary Related Articles Check |
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Current State of Data Mining - Drewry, Gu, Hocking, Kang, Schutt.. (2002) (Correct)
....underlying distribution (or measure) of the partitions can serve as a basis to calculate and estimate the confidence of a rule. There have been many extensions to RSDA. Among them, variable precision rough set model(VPRS) 41] rough mereology [29] and similarity or indiscernibility based RSDA [17]. 15 5 Quantitative Rule Mining Numeric data comprises a significant proportion of real world databases. Despite this fact, the majority of data mining approaches have focused solely on the extraction of categorical association rules. The nature of the problem of mining quantitative association ....
X. Hu and N. Cercone. Rough Set Similarity Based Learning from Databases. In Proc. of The first International Conference on Knowledge Discovery and Data Mining, pages 162--167, 1995.
Rough Set Data Analysis - Düntsch (2000) (1 citation) (Correct)
.... [34] Thus, similarity relations, which are reflexive and symmetric, but not necessarily transitive, have been studied inside RSDA in some detail (for example, in rough mereology) and many of the notions of indiscernibility based RSDA have been translated and adjusted to the new situation [23, 27, 55, 67]. The logical structure of the resulting systems has been investigated in [26, 76, 77] Ordinal prediction takes into account numerical information of the domain of the decision attribute; its rules predict intervals rather than unique values and are of the form If f # #x##a: thena # f # #x# # ....
Hu, X. & Cercone, N. (1995). Rough set similarity based learning from databases. In Proc. of the First International Conference on Knowledge Discovery and Data Mining, 162--167.
Discovery Of Multiple-Level Rules From Large Databases - Fu (1996) (6 citations) (Correct)
....which still keeps the dependency, i.e. POS(P 0 ; Q) POS(P;Q) Decision table are then derived from the minimal set. Decision rules (or classification rules) can be obtained from the decision table. Hu and Cercone integrated rough sets with attribute oriented induction to find high level rules [57]. A set of objects are first generalized using attribute oriented induction [45] The rough sets method is then applied on these generalized objects to find the decision table at a general level. Rough sets provide a tool for KDD with a solid mathematical foundation. However, it can only discover ....
X. Hu and N. Cercone. Rough set similarity-based learning from databases. In Proc. First Int. Conf. on Knowledge Discovery and Data Mining, pages 162--167, Montreal, Canada, Aug. 1995. BIBLIOGRAPHY 180
Knowledge Discovery In Databases: An Attribute-Oriented Rough Set.. - Hu (1995) (8 citations) Self-citation (Hu) (Correct)
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X. Hu, N. Cercone, (1995). Rough Sets Similarity-Based Learning From Databases, accepted in the 1st International Conference on Knowledge Discovery and Data Mining, Montreal, Canada, Aug. 21-23, 1995
Searching For Relational Patterns In Data - Nguyen, Skowron (1997) (Correct)
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Hu X., Cercone N., Rough Set Similarity Based Learning from Databases. Proc of The Fourth International Workshop on Rough Set, Fuzzy Set and Machine Discovery. August 20-21,1995 Montreal, Canada, pp.162-167.
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