Distance Based Algorithm for Effective Outliers Classification and Prediction of WDBC Dataset

  Dr. D. Rajakumari

Knowledge Discovery on Database (KDD) is an essential process on data processing. Many features selection and classification algorithms are used to select the relevant features and classified in data mining applications. The outlier detection is presently growing as an extensive task in the data mining applications. Many outlier detection techniques were developed previously to overcome the challenges in the detection of outliers. Many feature selection and classification algorithms are used to select the relevant features and classify them according to criteria in data mining applications. These techniques suffer some limitations due to increasing complexity, size and variety of data sets. This paper proposes a novel boundary based classification approach for the effective prediction of outliers.

Keywords- Boundary Based Classification, Data Mining Outlier Detection, Wisconsin Breast Cancer Dataset

Unique Identification Number - IJEDR1803003
Page Number(s) - 11-16
Pubished in - Volume 6 | Issue 3 | July 2018
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Dr. D. Rajakumari,   "Distance Based Algorithm for Effective Outliers Classification and Prediction of WDBC Dataset", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.6, Issue 3, pp.11-16, July 2018, Available at :http://www.ijedr.org/papers/IJEDR1803003.pdf