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1. Data-Reduction-Algorithm-Identification-and-Elimination-of-Erroneous-rows Data-Reduction-Algorithm-Identification-and-Elimination-of-Erroneous-rows Public

   The proposed algorithm calculates the Initial Recall value of the Dataset. It eliminates least correlated features using the Correlation Matrix. Using Gaussian Curve, for all the columns it identif…

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2. Formation-of-Dense-Clusters-by-Outlier-Elimination-and-Standard-Deviation Formation-of-Dense-Clusters-by-Outlier-Elimination-and-Standard-Deviation Public

   This method suggests a technique for removing outliers that takes standard deviation into account.

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3. Recession-Domain-Adapted-Feature-Selection-Technique Recession-Domain-Adapted-Feature-Selection-Technique Public

   A Novel Methodology of Domain Wise feature selection approach which is capable of identifying the interrelationships by focusing on Domain-Wise feature selection. It ensures that correlated and sim…

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4. Skewness-Based-Outlier-Elimination-technique-for-Pima-Diabetes-Dataset Skewness-Based-Outlier-Elimination-technique-for-Pima-Diabetes-Dataset Public

   The proposed algorithm is successful in elimination of 108 rows from Pima Diabetes Dataset by skewness range of Normal distribution curve. To check the efficacy of the algorithm, it is compared wit…

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5. New-Approaches-to-Robust-Homogeneous-And-Clearly-Identifiable-Cluster-Creation New-Approaches-to-Robust-Homogeneous-And-Clearly-Identifiable-Cluster-Creation Public

   A new clustering technique is proposed that incorporates outliers during clustering. The proposed approach involves using a variable, (λ > 0), to define the cluster radius. Weighted an

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6. Parkinsons-Upsampling-algorithm Parkinsons-Upsampling-algorithm Public

   Developed a novel upsampling algorithm which accurately identifies impactful rows from the dataset

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