Add to ORKGin 2013. He worked for Yahoo! Bangalore for two years. His research interests are in the areas o
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
Abstract- Outlier detection is an active area for research in data set mining community. Finding out...
Description routines for univariate and multivariate outlier detection with a focus on parametric me...
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a larg...
Abstract—In the statistics community, outlier detection for time series data has been studied for de...
Abstract — A phenomenal interest in big data among research community has emerged. Outlier detection...
Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, ...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Abstract — Outlier detection in vehicle traffic data is a practical problem that has gained traction...
The fast growing of data observed in recent years does not seem to slow down. An increasing interest...
This study attempts to better understand the impact of an outlier in time series model and the impor...
The outlier detection in the field of data mining and Knowledge Discovering from Data (KDD) is captu...
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
Abstract- Outlier detection is an active area for research in data set mining community. Finding out...
Description routines for univariate and multivariate outlier detection with a focus on parametric me...
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a larg...
Abstract—In the statistics community, outlier detection for time series data has been studied for de...
Abstract — A phenomenal interest in big data among research community has emerged. Outlier detection...
Outlier detection refers to the detection of unexpected situations in the data. Outliers are fraud, ...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach...
Abstract — Outlier detection in vehicle traffic data is a practical problem that has gained traction...
The fast growing of data observed in recent years does not seem to slow down. An increasing interest...
This study attempts to better understand the impact of an outlier in time series model and the impor...
The outlier detection in the field of data mining and Knowledge Discovering from Data (KDD) is captu...
Outlier Detection is a technique to detect anomalous events or outliers during analysis of the data ...
Abstract- Outlier detection is an active area for research in data set mining community. Finding out...
Description routines for univariate and multivariate outlier detection with a focus on parametric me...