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  Published Paper Details:

  Paper Title

OUTLIER DETECTION AND CLUSTERING IMPROVEMENT USING A NOVEL APPROACH

  Authors

  Jagannath Mahajan,  Dr. Jitendra Sheethlani

  Keywords

Outlier, Clustering, k-means clustering, Unsupervised

  Abstract


Outlier detection is utilised in a variety of applications, including clustering-based illness onset identification, gene expression analysis, computer network intrusion detection, financial fraud detection, and human behaviour analysis. Because of their poor accuracy and lack of a comprehensive strategy, existing methods for detecting outliers are ineffective. Small clusters are usually considered outliers, and most algorithms provide an outlier score to each data object. Due to high computational complexity and misidentification of regular data objects as outliers, these techniques have disadvantages. Using a modified k-means clustering technique, we provide a unique unsupervised approach for detecting outliers in this study. To increase clustering accuracy, outliers are eliminated from the dataset. By comparing our method to existing approaches and benchmark performance, we are able to prove that it is effective.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2112579

  Paper ID - 222216

  Page Number(s) - f367-f376

  Pubished in - Volume 9 | Issue 12 | December 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Jagannath Mahajan,  Dr. Jitendra Sheethlani,   "OUTLIER DETECTION AND CLUSTERING IMPROVEMENT USING A NOVEL APPROACH", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 12, pp.f367-f376, December 2021, Available at :http://www.ijcrt.org/papers/IJCRT2112579.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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