Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)

Detecting Suspicious Card Transactions in unlabeled data of bank Using Outlier Detection Techniqes

With the advancement of technology, the use of ATM and credit cards are increased. Cyber fraud and theft are the kinds of threat which result in using these Technologies. It is therefore inevitable to use fraud detection algorithms to prevent fraudulent use of bank cards. Credit card fraud can be thought of as a form of identity theft that consists of an unauthorized access to another person's ...

Risk Assessment Using Local Outlier Factor Algorithm

In this paper we introduce the unsupervised machine-learning algorithm named Local Outlier Factor (LOF), for health risk assessment. In general the LOF algorithm is used with numerical attributes and the outcome of the algorithm is parting the patterns into normal and abnormal events. In this paper we introduce the extended LOF algorithm with three experimental contributions: (i) utilization of...

Local Outlier Detection with Interpretation

Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that exp...

FP-outlier: Frequent pattern based outlier detection

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...

Self-Adaptive Negative Selection Using Local Outlier Factor