An efficient data mining process on temporal data using relevance feedback method
by D. Yuvaraj; Mavaluru Dinesh; M. Sivaram; S. Nageswari
World Review of Science, Technology and Sustainable Development (WRSTSD), Vol. 18, No. 1, 2022

Abstract: Some data is historical such as the judicial or medical which the factor of time is very important. Temporal databases can deal with these data because it provides a systematic way of dealing with historical data. The temporal data mining deals with these types of data, that has time stamping and influences by the factor of time after mining. The relevance feedback technique is included in the individual's revisitation habits and memory strength. We have additionally executed and assessed the performance by utilising a trace driven methodology dependent on the online real behaviour dataset. We also presented the large level datasets introducing a profile that encode the user subjective notation of similarity in domain. These profiles can be gained ceaselessly from connection with client. We further show how the client profile might be embedded in a system that utilisation relevance feedback mechanism. At long last, we compute the scalability of our system by utilising various datasets from the various domains.

Online publication date: Wed, 01-Dec-2021

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