A survey on novelty detection using level set methods

The process of mining includes various methodologies and data classification is one of the advantageous methods involved in it. It not only eases the process of machine learning but also gives a platform for proper functioning of the process. There are cases wherein the data which is important or unidentified is missed during the process of classification. The process of mining is highly affected by the absence of such kind of data. The process of extraction of unknown and new data from the huge dataset that has been left during the classification process is known as novelty detection. This paper reviews the identification of novelty data that is used during the process of classification. Novelty detection generally focuses on the identification of shapes or patterns, space and learning along with density taxonomy models. This paper aims to consider the most typically used ideas in modeling the novelty data to classify and compare the schemes discussed in existing literature as well as to provide useful material for existing state and challenges for the same.