A Survey on Different Unsupervised Techniques to Detect Outliers
Shruti S. Rakhe, Archana Suhas Vaidya · 2015
In data mining outlier detection refers to the recognition of data point which does not follow the expected pattern or behavior in a particular dataset or is significantly different from other points in a data. In this paper we will review some of the outlier detection techniques and discuss their advantages and disadvantages with respect to various aspects. Outlier detection techniques can be classified into three modes namely unsupervised, semi-supervised and supervised. But, unsupervised outlier detection methods can be further classified as distance based or density based. Many outlier detection techniques are proposed till date. These proposed techniques can be broadly categorized as distribution based (statistical), clustering-based, density-based and model-based