A Review: Outlier Detection using Various Clustering Algorithm in Data Mining

Data mining is the extraction of hidden predictive information from large databases and also a powerful new technology with great potential to analyze important information in their data warehouses. There are several techniques exist for data extraction. Clustering is one of the techniques amongst them. In clustering technique, we 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. There are so many techniques existing to detect outlier but Clustering is one of the efficient techniques. In this paper, I have review on different Clustering techniques in terms of time complexity and proposed a new solution by adding DBSCAN to already present Clustering techniques.