Comparative Study of Outlier Detection Algorithms

Kaur, Kanwaljit; Garg, Atul

doi:10.5120/ijca2016911176

Cited by 5 publications

(2 citation statements)

References 19 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, the comparison of those outlier detection algorithms must rely on some specific measures which test the quality of the technique. Some of the studies of Kamaljeet Kaur and Atul Garg [9] discusses the brief view over empirical analysis of outlier detection algorithms. As part of their conclusion Clustering and Classification, based outlier detection algorithms are efficient and highly scalable, the computational cost is low.…”

Section: Classification Based On the Type Of Supervisionmentioning

confidence: 99%

A Supervised Approach for Detection of Outliers in Healthcare Claims Data

Jyothi¹,

Lakshmi²,

Rao³

2020

JESTR

View full text Add to dashboard Cite

Outlier detection is a fast-moving method in healthcare data and it is the major concern for the health insurance providers. Most of the Medicare data is related to real-world data. Outlier analysis plays a crucial role in data validity and reliability. To detect outlier for medical data is a complex task as it is having more number of variables and is of multivariate in nature. The paper presents a model-based approach in which outliers are detected and they were assigned with labels. The outlier or suspicious is defined as some outcome, which is expected that it is going to commit fraud. The methodology carried out in two phases to develop a Supervised Outlier Detection Approach in healthcare Claims (SODAC). Initially, the data preprocessing stage for feature selection it uses the filter method and set grouping hierarchy to select the best subset and to organize the features. The outlier detection phase uses the combination of classic methods of statistical and distance-based approach. To evaluate the distribution of data the Gaussian probability density function is applied for the data values. The distance-based approach which reflects the outputs as outlier codes. The partitioning of the input dataset and applies statistical mean to each subset and further uses derived multi aggregate metric to consolidate the data instances of the partitions(subsets). The distance-based outlier detection (dod) is done by calculating the maximum distance from the inner average statistical mean measure of the neighborhood from the data objects (instances) of the input. The data object value goes beyond the maximum or minimum of calculated measure is termed as suspicious. This type of detection of outliers is called as indicative fraud potential. The results performed relatively stable for a large scale data as illustrated in the experimentation part over publicly available real world data.

show abstract

Section: Classification Based On the Type Of Supervisionmentioning

confidence: 99%

A Supervised Approach for Detection of Outliers in Healthcare Claims Data

Jyothi¹,

Lakshmi²,

Rao³

2020

JESTR

View full text Add to dashboard Cite

show abstract

“…This is due to the fact that outliers can significantly affect data mining performance [14]. There are various reasons that can induce outlier in the data; some of them are malicious activities like credit card fraud, cyber activity, the breakdown of the system, mechanical faults, changes in system behavior [15].…”

Section: Introductionmentioning

confidence: 99%

An Improved Overlapping Clustering Algorithm to Detect Outlier

Danganan

Sison

Medina

2018

IJEEI

View full text Add to dashboard Cite

MCOKE algorithm assigns a data object to multiple clusters and is known for its simplicity and effectiveness. Its drawback is the use of maxdist as a global threshold in assigning objects to one or more cluster because it is sensitive to outliers. Having outliers in the datasets can significantly affect the effectiveness of MCOKE with regards to overlapping clustering. In this paper, the outlier detection MAD is incorporated in MCOKE algorithm so that it can detect and remove outliers that can participate in the assignment of objects to one or more clusters. Experiments demonstrate that the improved MCOKE algorithm with MAD provides better identification of overlapping clustering. The performance of the outlier detection was also evaluated via F1 score performance criterion. Evaluation results revealed that the outlier detection demonstrated higher accuracy rate in identifying outliers when applied to real datasets.

show abstract