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- W3119154110 abstract "The disparate data refers to a range of dissimilar data having varied data formats. Such data ischaracterised by diversity in dimensionality and often regarded as of inferior quality. In recent times,the data science realm is witnessing generation of humongous amounts of disparate data globally thatdemand efficient management of such data through various collaborative methods. Typically, BigData is defined as a loose ensemble disparate, vibrant, unreliable and interconnected data. Big Dataanalysis is applied to examine the association between factors and patterns of detection. In disparatedata, the hitherto unknown trends can be located through Big Data analysis. The disparate datacarries a tag of being inferior or being low quality as it suffers from missing values and high dataredundancy besides being inconsistent, vague, and noisy. This present review article discusses someof the challenges and problems found in modern big data models that deal with disparate data andproposes a novel approach to variable selection in high-dimensional data. The projected algorithmwill help in obtaining a good subset of features by efficiently treating irrelevant and superfluousfeatures." @default.
- W3119154110 created "2021-01-18" @default.
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- W3119154110 date "2020-10-17" @default.
- W3119154110 modified "2023-10-16" @default.
- W3119154110 title "EXTRACTION OF INTERESTING VARIABLES FROM DISPARATE DATA IN BIG DATA ANALYTICS" @default.
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