The number of methods available in data mining to detect the outlier by making the clusters of data and then detect the outlier from them. The objects that are similar to each other are organized in group it’s called cluster and the objects that do not comply with the model or general behavior of the data these data objects called outliers. Outliers detect by clustering. Density based clustering algorithm (DENCLUE) is one of the primary methods for clustering in data mining which groups neighboring objects into clusters based on local density conditions rather than proximity between objects. Data points are assigned to a cluster by hill climbing, points going to the same local maximum are put into the same cluster. The traditional density estimation only considers the location of the point, not variable of interest. Depending on the convergence criteria, the method needs less iteration as fixed step size methods and improving cluster quality and also finding an outlier correctly.

Clustering, Data Mining, Density Based Clustering Algorithm, DBSCAN, OPTICS, Outlier Mining

[1]. Harsh Shah, Karan Napanda and Lynette D’mello, “Density Based Clustering algorithm”, International Journal of Computer Engineering, vol. 3,Issue. 11, pp.54-57, Nov 2015, E-ISSN: 2347-2693
[2]. Anoop Kumar Jain, Prof.Satyam Maheswari,“Survey of Recent Clustering Techniques in Data Mining”, International Journal of Computer Science and Management Research ,Vol. 1, Issue 1, Aug 2012.
[3]. R.J Bolton, D.J.Hand, ”Statistical fraud detection”: A review with discussion, Statistical Science, 17(3): pp. 235-255, 2002.
[4]. E. Eskin , A.Arnold , M.Prerau , L.Portnoy , S.Stolfo ,” A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data”, In Data Mining for Security Applications, 2002.
[5]. E. Knorr, R.Ng., V. Tucakov, ,” Distance-based outliers: Algorithms and applications”, The International Journal on Very Large Data Bases Journal 8, pp. 237–253, 2000.
[6]. J.Laurikkala, M. Juhola and E. Kentala, “Informal Identification of Outliers in Medical Data”, in Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology IDAMAP-2000 Berlin,Organized as a workshop of the 14th European Conference on Artificial Intelligence ECAI-2000.
[7]. M.Breunig, H.Kriegel,R. Ng , J.Sander, “LOF: Identifying density based local outliers”, In: Proc. SIGMOD Conf, pp. 93–104, 2000.
[8]. C.Aggarwal,P.S. Yu , “Outlier Detection for High Dimensional Data”. In: Proceedings of the ACM SIGMOD Conference 2001.
[9]. Anant Ram, Sunita Jalal, S. Anand . Jalal, Manoj kumar, “A density Based Algorithm for Discovery Density Varied cluster in Large spatial Databases”, International Journal of Computer Application Vol. 3,No.6, June 2010.
[10]. R.Prabahari ,V.Thiagarasu , “A Comparative Analysis of Density Based Clustering Techniques for Outlier Mining”, International Journal Of Engineering Sciences & Research Technology, ISSN 2277-9655, pp 132-136 November, 2014.
[11]. Alexander Hinneburg, Daniel A.Keim (1998),"An Efficient Approach to Clustering in Large Multimedia Databases with Noise [Online] Available: http://www.aaai.org
[12]. B.G Obula Reddy, Dr. Maligela Ussenaiah,“Literature Survey On Clustering Techniques”, IOSR Journal of Computer Engineering, Vol. 3, Issue 1, July 2012.
[13]. Mariam Rehman, Syed Atif Mehdi,“Comparison of Density Based Clustering Algorithms”, research work, Lahore College for Women University, Lahore, Pakistan.
[14]. Henrik Bäcklund, Anders Hedblom, Niklas Neijman, “DBSCAN A Density-Based Spatial Clustering of Application with Noise”, 2011.
[15]. Anoop Kumar Jain, Prof.Satyam Maheswari,“Survey of Recent Clustering Techniques in Data Mining”, International Journal of Computer Science and Management Research ,Vol. 1, Issue 1, Aug 2012.
[16]. Rui Xu, Donald Wunsch,“Survey of Clustering Algorithms”, IEEE Transactions On Neural Netwoks, Vol. 16, No. 3, May 2005
[17]. R.Prabahari ,V. Thiagarasu , “Density Based Clustering Using Gaussian Estimation Technique” , International Journal on Recent and Innovation Trends in Computer Science and Communication(IJRITCC), ISSN 2321-8169, pp 4078-4081 December, 2014.
[18]. Jianhao Tan and Jing Zhang “An Improved Clustering Algorithm Based on Density Distribution Function” Computer and Information Science Vol. 3, No. 3; August 2010.
[19]. He Zengyou, Xu Xiaofei , Deng Shengchun, Squeezer, “An efficient algorithm for clustering categorical data”, Journal of Computer Science and Technology, pp. 611-624, May 2002.
[20]. Shweta Verma, Vivek Badhe “Survey on Big Data and Mining Algorithm” International Journal of Scientific Research in Science, Engineering and Technology, Vol. 2 ,May 2016 Online ISSN : 2394-4099.