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Outlier Analysis of Categorical Data using Artificial Intelligence


Jyotsna Diliprao Thakre , V.M. Institute of Engineering & Technology (VMIT), Nagpur, India; Gurudev B Sawarkar, V.M. Institute of Engineering & Technology (VMIT), Nagpur, India


Outlier Detection, Stream Data Mining, Local Outlier, Memory Efficiency


Outlier Mining is an essential job of discovering the data records that have a special performance comparing with other records in remaining dataset. Outliers do not follow with other data objects in the dataset. There are many practical approaches to detect outliers in numerical data. Most of the earliest work on outlier detection was performed by the statistics community on numeric data. But for categorical dataset there are limited approaches by using Memory efficient Incremental Local Outlier (MiLOF) detection algorithm and ROAD (Ranking-based Outlier Analysis and Detection algorithm).

Other Details

Paper ID: IJSRDV7I20117
Published in: Volume : 7, Issue : 2
Publication Date: 01/05/2019
Page(s): 367-371

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