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Many applications today need to manage data that is uncertain, such as information extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top-k queries are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering top-k queries in a probabilistic framework from a state-of-the-art statistical IE model-semi-Conditional...
One of the main obstacles facing current data mining techniques is attribute reduction. This paper discusses the basic concepts of rough set, and studies two rough approximations operators under neighborhood systems. An attribute reduction method based on rough set theory and neighborhood systems is presented. The experimental results show that the method of attributes reduction with rough sets and...
Rough set theory is an efficient information processing tool used in the discovery of data dependencies. It evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. This paper presents a method for attribute reduction on combination of rough set and neighborhood systems. Neighborhood decision system...
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