Detection of Anomalous Value in Data Mining.

6 pages•Published: October 23, 2018

Darshanaben Dipakkumar Pandya and Sanjay Gaur

Abstract

In the database of numeric values, outliers are the points which are different from other values or inconsistent with the rest of the data. They can be novel, abnormal, unusual or noisy information. Outliers are more attention-grabbing than the high proportion data. The challenges of outlier detection arise with the increasing complexity, mass and variety of datasets. The problem is how to manage outliers in a dataset, and how to evaluate the outliers. This paper describes an advancement of approach which uses outlier detection as a pre-processing step to detect the outlier and then applies rectangle fit algorithm, hence to analyze the effects of the outliers on the analysis of dataset.

Keyphrases: anomalous values, attribute, data mining, quartiles., rectangle fit algorithm

In: Vinay K Chandna, Vijay Singh Rathore and Shikha Maheshwari (editors). Proceedings on International Conference on Emerging Trends in Expert Applications & Security (2018), vol 2, pages 1-6.

Links:	https://easychair.org/publications/paper/S5Z3
	https://doi.org/10.29007/6xfn

BibTeX entry

@inproceedings{ICETEAS2018:Detection_Anomalous_Value_Data,
  author    = {Darshanaben Dipakkumar Pandya and Sanjay Gaur},
  title     = {Detection of Anomalous Value in Data Mining.},
  booktitle = {Proceedings on International Conference on Emerging Trends in Expert Applications & Security (2018)},
  editor    = {Vinay K Chandna and Vijay Singh Rathore and Shikha Maheshwari},
  series    = {Kalpa Publications in Engineering},
  volume    = {2},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1770},
  url       = {/publications/paper/S5Z3},
  doi       = {10.29007/6xfn},
  pages     = {1-6},
  year      = {2018}}

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