Search (5 results, page 1 of 1)

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Abstract

Examines basic issues of data mining using the theory of rough sets, which is a recent proposal for generalizing classical set theory. The Pawlak rough set model is based on the concept of an equivalence relation. A generalized rough set model need not be based on equivalence relation axioms. The Pawlak rough set model has been used for deriving deterministic as well as probabilistic rules froma complete database. Demonstrates that a generalised rough set model can be used for generating rules from incomplete databases. These rules are based on plausability functions proposed by Shafer. Discusses the importance of rule extraction from incomplete databases in data mining

Footnote

Contribution to a special issue devoted to knowledge discovery and data mining

Type

a

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Abstract

The subject of query formulation is analysed within the framework of adaptive linear models. The study is based on the notions of user preference and an acceptable ranking strategy. A gradient descent algorithm is used to formulate the query vector by an inductive process. Presents a critical analysis of the existing relevance feedback and probabilistic approaches.

Type

a

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Abstract

The notion of user preference is adopted for the representation, interpretation, and measurement of the relevance or usefulness of documents. Use judgements on documents may be formally describes by a weak order (i.e. user ranking) and measured using an ordinal scale. Within this framework, a new measure of system performance is suggested based on the distance between user ranking and system ranking. It only uses the relative order of documents and therefore confirms to the valid use of an ordinal scale measuring relevance. It is also applicable to multilevel relevance judgements and ranked system output. The appropriateness of the proposed measure is demonstrated through an axiomatic approach. The inherent relationships between the new measure and many existing measures provide further supporting evidence

Type

a

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Abstract

Reports on the experimental evaluation of an adaptive linear model that constructs improved user query vectors from user preference judgements on a sample set of documents. The performance of this method is compared with that of the standard relevance feedback techniques. The experimental results seem to demonstrate the effectiveness of the adaptive method

Type

a

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Type

a