Minimization of Datasets : Using a Master Interlinked Dataset
DOI:
https://doi.org/10.17010/ijcs/2018/v3/i5/138778Keywords:
Controlled Datasets
, Dataset Binary Encoding, Machine Learning, Master Datasets, Progressive Sampling of Data.Publishing Chronology Manuscript received July 28
, 2018, revised August 12, accepted August 14, 2018. Date of publication September 6, 2018Abstract
We all know there are a lot of datasets. Each data set corresponds to the contents of a single statistical database. Datasets have several properties based on statistical measures applicable to the number and type of attributes or variables. Here, the focus is mainly on statistics i.e., sampling of data based on observation and analysis. Each data of a dataset is sampled quantitatively by doing binary encoding. Sampling of a dataset using a predictor can often result in error. However, these errors can have a trend that might be related to one or more datasets. This can differentiate every variable of one dataset from remaining datasets. All these datasets can be unified into a single master dataset based on user requirements.Downloads
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Published
2018-10-01
How to Cite
Haider, S. A., & Patil, N. S. (2018). Minimization of Datasets : Using a Master Interlinked Dataset. Indian Journal of Computer Science, 3(5), 20–24. https://doi.org/10.17010/ijcs/2018/v3/i5/138778
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References
Dataset [Online]. Available: https://en.wikipedia.org/wiki/Data_set
Linear Regression.[Online]. Available: https://en.wikipedia.org/wiki/Linear_regression
Encoding (memory) [Online]. Available: https://en.wikipedia.org/wiki/Encoding_(memory)