Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 1893-1903 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8668049420/2020©BEIESP | DOI: 10.35940/ijeat.D8668.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Finding linear sequential relationships (LSRs) in the data and applying them for obtaining fruitful results is an essential task in many modern day to day useful real and simulation based applications. In many previous research applications many research people usually assumed that there exists certain relationships in the data and then they have tried to bring forth some useful results after processing the selected datasets using one more data mining, machine leaning, and big data techniques. Take it for granted assumptions on the data may not be true in all the cases and in all the applications. The purpose of the present study is to bring out some automatic, smart, simple, scalable, fruitful and useful data analysis techniques after analyzing the datasets in the hand and at the same time assumptions on the data are not considered just like the general fashion of take it for granted option. The proposed model is particularly useful and applicable for finding the drug to disease relationships.
Keywords: Automatic Finding, Data, Direct, Linear Sequential Relationships.