Applying Rough Sets to Maintain Data Consistency for High Degree Relations

Sug, Hyontai

doi:10.1109/ncm.2008.180

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…ANALYSIS BASED ON REDUCT AND CORE By the above procedure we can extract Core of condition attributes which are explicitly necessary for deriving knowledge and coming to some conclusions related to the extraction of knowledge [2], [4]. We need to pursue a method which would give information of whether a particular characteristic attribute is necessary or not, based on which it can be established whether a patient has influenza or not.…”

Section: Creation Of Decision Table For Knowledge Discoverymentioning

confidence: 99%

“…Analysis over the decision table is performed in this paper by identifying those core attributes whose removal would results in further inconsistency in the decision table which was consistent other wise. In the above decision table [2][3] (Table II.) by dropping F 1 rules P 2 and P 3 turns out to be inconsistent and positive region of the algorithm changes. Therefore, F 1 forms the core of the attribute set in the decision table.…”

Section: Creation Of Decision Table For Knowledge Discoverymentioning

confidence: 99%

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Identification and Evaluation of Functional Dependency Analysis using Rough sets for Knowledge Discovery

Sreevani¹,

Rao²

2010

IJACSA

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Abstract-The process of data acquisition gained momentum due to the efficient representation of storage/retrieving systems. Due to the commercial and application value of these stored data, Database Management has become essential for the reasons like consistency and atomicity in giving birth to DBMS. The existing database management systems cannot provide the needed information when the data is not consistent. So knowledge discovery in databases and data mining has become popular for the above reasons. The non-trivial future expansion process can be classified as Knowledge Discovery. Knowledge Discovery process can be attempted by clustering tools. One of the upcoming tools for knowledge representation and knowledge acquisition process is based on the concept of Rough Sets. This paper explores inconsistencies in the existing databases by finding the functional dependencies extracting the required information or knowledge based on rough sets. It also discusses attribute reduction through core and reducts which helps in avoiding superfluous data. Here a method is suggested to solve this problem of data inconsistency based medical domain with a analysis.

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Section: Creation Of Decision Table For Knowledge Discoverymentioning

confidence: 99%

Section: Creation Of Decision Table For Knowledge Discoverymentioning

confidence: 99%