Outlier Detection: Applications and techniques in Data Mining

Outlier Detection is one of the major issues in Data Mining; finding outliers from a collection of patterns is a popular problem in the field of data mining. An outlier is that pattern which is dissimilar with respect to all the remaining patterns in the data set. Outlier detection is quiet familiar area of research in mining of data set. It is a quiet important task in various application domains. Earlier outliers considered as noisy data, has now become severe difficulty which has been discovered in various domains of research. The discovery of outlier is useful in detection of unpredicted and unidentified data, in certain areas like fraud detection of credit cards, calling cards, discovering computer intrusion and criminal behaviors etc. A number of surveys, research and review articles cover outlier detection techniques in great details. Here in this review paper, my effort is to take as one several techniques of outlier detection. By this attempt, we wish to gain a improved perceptive of various research on outlier detection and analysis for our well-being as well as for those who are the beginners in this field, so that they can easily pickup the links in details.

[1]  Chang-Tien Lu,et al.  Detecting spatial outliers with multiple attributes , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[2]  Sridhar Ramaswamy,et al.  Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD '00.

[3]  Chang-Tien Lu,et al.  Spatial Weighted Outlier Detection , 2006, SDM.

[4]  Philip S. Yu,et al.  Outlier detection for high dimensional data , 2001, SIGMOD '01.

[5]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[6]  Anthony K. H. Tung,et al.  Mining top-n local outliers in large databases , 2001, KDD '01.

[7]  Douglas M. Hawkins.  Identification of Outliers , 1980, Monographs on Applied Probability and Statistics.

[8]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD 2000.

[9]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[10]  Vipin Kumar,et al.  Feature bagging for outlier detection , 2005, KDD '05.

[11]  Raymond T. Ng,et al.  Distance-based outliers: algorithms and applications , 2000, The VLDB Journal.

[12]  Shashi Shekhar,et al.  Spatial Databases: A Tour , 2003 .

[13]  Christos Faloutsos,et al.  LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[14]  S. Muthukrishnan,et al.  Mining Deviants in a Time Series Database , 1999, VLDB.

[15]  Raymond T. Ng,et al.  Finding Intensional Knowledge of Distance-Based Outliers , 1999, VLDB.

[16]  ShimKyuseok,et al.  Efficient algorithms for mining outliers from large data sets , 2000 .

[17]  Steven K. Donoho,et al.  Early detection of insider trading in option markets , 2004, KDD.

[18]  Chang-Tien Lu,et al.  Spatial Outlier Detection: A Graph-Based Approach , 2007 .

[19]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..