BibTeX:

@article{IJIRSTV3I5078,
     title={A Review Paper on Comparison of Clustering Algorithms based on Outliers},
     author={Shivanjli Jain and Amanjot Kaur},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={5},
     pages={178--182},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I5078.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Data mining, in general, deals with the discovery of non-trivial, hidden and interesting knowledge from different types of data. With the development of information technologies, the number of databases, as well as their dimension and complexity, grow rapidly. It is necessary what we need automated analysis of great amount of information. The analysis results are then used for making a decision by a human or program. One of the basic problems of data mining is the outlier detection. The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this thesis, the ability to detect outliers can be improved using a combined perspective from outlier detection and cluster identification. In proposed work comparison of four methods will be done like K-Mean, k-Mediods, Iterative k-Mean and density based method. Unlike the traditional clustering-based methods, the proposed algorithm provides much efficient outlier detection and data clustering capabilities in the presence of outliers, so comparison has been made. The purpose of our method is not only to produce data clustering but at the same time to find outliers from the resulting clusters. The goal is to model an unknown nonlinear function based on observed input-output pairs. The whole simulation of this proposed work has been taken in MATLAB environment.

Mining, Clustering, Outlier, Data Mining Methods