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Discovery of Outlier from Database using Different Clustering Algorithms


Affiliations
1 Computer Science, B.R.C.M.
2 Department of Computer Science, B.R.C.M
 

Data mining is the process of extracting valuable and important data from a large or massive set of data. There are several techniques existing 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 is one of the major issues in data mining. Outlier represents that data which possess different behavior from others. 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 compared the result of different techniques with the result of Clustering techniques in terms of time complexity and proposed a new solution by adding fuzziness to already existing Clustering techniques.

Keywords

Data Mining, Clustering, Outlier Detection
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  • Ajay Challagalla, Shivaji Dhiraj SS, Somayajulu, DVLN, Toms Shaji Mathew, Saurav Tiwari, Syed Sharique Ahmad (2010) Privacy Preserving Outlier Detection Using Hierarchical Clustering Methods, 34th Annual IEEE Computer Software and Applications Conference Workshops.

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  • Jiang S & An, Q (2008) Clustering Based Outlier Detection Method, Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

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  • Discovery of Outlier from Database using Different Clustering Algorithms

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Authors

Deepak Soni
Computer Science, B.R.C.M.
Naveen Jha
Department of Computer Science, B.R.C.M
Deepak Sinwar
Department of Computer Science, B.R.C.M

Abstract


Data mining is the process of extracting valuable and important data from a large or massive set of data. There are several techniques existing 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 is one of the major issues in data mining. Outlier represents that data which possess different behavior from others. 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 compared the result of different techniques with the result of Clustering techniques in terms of time complexity and proposed a new solution by adding fuzziness to already existing Clustering techniques.

Keywords


Data Mining, Clustering, Outlier Detection

References