Higher Dimensional Data Access and Management with Improved Distance Metric Access for Higher Dimensional Non-Linear Data
Sakshi Jolly1, Neha Gupta2
1Sakshi Jolly, Research Scholar, Faculty of Computer Applications Department, MRIIRS, Faridabad (India).
2Dr. Neha Gupta, Associate Professor, Faculty of Computer Applications Department, MRIIRS, Faridabad (India).

Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5692-5697 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8322118419/2019©BEIESP | DOI: 10.35940/ijrte.D8322.118419

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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: Distance metrics for different kinds of data we daily use is the common approach for indentifying the insights of information and identifying the noisy and resolving the information with different scenarios and rules. The methodology imposed here is different in kinds of rules and the information we provide to the knowledge machine is the most important and considerable thing in designing and implementing distance metrics. The contradictory data is mixed information which the dataset includes is having the irrelevant information with the relevant information and identifying the novel thing from the information gathered. The information and the data gathered will be in the form of different formats of data and the most frequent thing we use is to make the clusters. In this article machine learning applications and the different data mining distance metric algorithms will be discussed and the information passed to the machine will be the ultimate and the dataset making is the quite challenging. In machine learning implementation the path of identifying the concept behind the everything to be predicted. The prediction works when the data is accurate and the information we get from the different repositories. All the data captured is not genuine and the combination of different such kind repositories make the contradicting data. The usage the additional distance metrics to manipulate and calculate the relation between the variables or the features we are considering. The machine learning is the finite mechanism which can help the researchers to identify the relationships between the variables or there is chance to find the inner relations among this kind of contradictory information. Contradictory data can help to identify the inner relations which can’t be identified with normal distance metrics and here used a little advanced and succeeded in reaching the optimal result.
Keywords: Machine Learning, Prediction, Contradictory Data, Result, Noisy Data.
Scope of the Article: Machine Learning.