ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining

Outlier Detection using Graph Mining

Community Trend Outlier Detection Using Soft Temporal Pattern Mining

Numerous applications, such as bank transactions, road traffic, and news feeds, generate temporal datasets, in which data evolves continuously. To understand the temporal behavior and characteristics of the dataset and its elements, we need effective tools that can capture evolution of the objects. In this paper, we propose a novel and important problem in evolution behavior discovery. Given a ...

Outlier Detection for Business Intelligence using Data Mining Techniques

In this paper we have made a review of various outlier detection techniques from data mining perspective. Existing studies in data mining focus generally on finding patterns from large datasets and using it for organizational decision making. However, finding exceptions and outliers did not receive much attention in the data mining field as other topics received. Finally, this paper concludes s...

Enhanced Outlier Detection Method using Association Rule Mining Technique

In recent years, much attention has been given to the problem of outlier detection, where the aim is to identify data which behave in an unexpected way or have abnormal properties. This paper focuses on the development of non-parametric association based algorithm to effectively identify classes and outliers simultaneously. The proposed algorithm identifies outlier transaction by enhancing an a...

Outlier Detection Using Distributed Mining Technology In Large Database

In many data analysis tasks, a large number of variables are being recorded or sampled. One of the first steps towards obtaining a coherent analysis is the detection of outlaying observations. A distributed approach is presented to detect distance-based outliers, based on the concept of outlier detection solving set. Data objects, which are different from or inconsistent with the remaining set ...