Outlier mining techniques for uncertain data

Ms. Aditi Dighavkar, Prof. N. M. Shahane

ABSTRACT Outlier detection has been a very significant concept in the realm of data analysis. Lately, many application domains have realized the direct relation between outliers in data and real world anomalies that are of immense interest to an analyst. Mining of outliers has been researched within vast application domains and knowledge disciplines. This paper provides a comprehensive overview of existing outlier mining techniques by classifying them along different dimensions. The motive of this survey is to identify the important dimensions which are associated with the problem of outlier detection, to provide taxonomy to categorize outlier detection techniques along the different dimensions. Also, a comprehensive overview of the recent outlier detection literature is presented using the classification framework. The classification of outlier detection techniques depending on the applied knowledge discipline can provide an idea of the research done by varied communities and also uncover the research avenues for the outlier detection problem. Keywords: Outlier Detection, Anomaly Detection, Likelihood value, Data classification

Title:	Outlier mining techniques for uncertain data
Author Name:	Ms. Aditi Dighavkar, Prof. N. M. Shahane
Abstract:	ABSTRACT Outlier detection has been a very significant concept in the realm of data analysis. Lately, many application domains have realized the direct relation between outliers in data and real world anomalies that are of immense interest to an analyst. Mining of outliers has been researched within vast application domains and knowledge disciplines. This paper provides a comprehensive overview of existing outlier mining techniques by classifying them along different dimensions. The motive of this survey is to identify the important dimensions which are associated with the problem of outlier detection, to provide taxonomy to categorize outlier detection techniques along the different dimensions. Also, a comprehensive overview of the recent outlier detection literature is presented using the classification framework. The classification of outlier detection techniques depending on the applied knowledge discipline can provide an idea of the research done by varied communities and also uncover the research avenues for the outlier detection problem. Keywords: Outlier Detection, Anomaly Detection, Likelihood value, Data classification
Cite this article:	Ms. Aditi Dighavkar, Prof. N. M. Shahane , " Outlier mining techniques for uncertain data " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 10, October 2014 , pp. 170-176 , ISSN 2319 - 4847.
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