Data Management

Differentiate Clustering Approaches for Outlier Detection

Free registration required

Executive Summary

Data mining is a process of extracting hidden and useful information from the data and the knowledge discovered by data mining is previously unknown, potentially useful, and valid and of high quality. There are several techniques exist for data extraction. Clustering is one of the techniques amongst them. In clustering technique, the authors form the group of similar objects (similarity in terms of distance or there may be any other factor). Outlier detection as a branch of data mining has many important applications and deserves more attention from data mining community. Therefore, it is important to detect outlier from the extracted data.

  • Format: PDF
  • Size: 220.64 KB