report.bbl

\begin{thebibliography}{1}

\bibitem{machine_data}
Curt Monash
\newblock "Three broad categories of data."
\newblock \url{http://www.dbms2.com/2010/01/17/three-broad-categories-of-data/}

\bibitem{splunk}
Splunk, Inc.
\newblock "Big Data Analytics."
\newblock \url{http://www.splunk.com/view/big-data/SP-CAAAGFH}

\bibitem{chandola}
Chandola, Varun, Arindam Banerjee, and Vipin Kumar.
\newblock "Anomaly detection: A survey."
\newblock \emph{ACM Computing Surveys (CSUR)} 41.3 (2009): 15.

\bibitem{chandola2}
Chandola, Varun, Arindam Banerjee, and Vipin Kumar.
\newblock "Anomaly detection for discrete sequences: A survey."
\newblock \emph{Knowledge and Data Engineering, IEEE Transactions on} 24.5 (2012): 823-839.

\bibitem{chandola3}
Chandola, Varun.
\newblock "Anomaly detection for symbolic sequences and time series data."
\newblock \emph{Dissertation.} University of Minnesota, 2009.

\bibitem{hodge}
Hodge, Victoria, and Jim Austin.
\newblock A survey of outlier detection methodologies.
\newblock \emph{Artificial Intelligence Review} 22.2 (2004): 85-126.

\bibitem{agyemang}
Agyemang, Malik, Ken Barker, and Rada Alhajj.
\newblock "A comprehensive survey of numeric and symbolic outlier mining techniques."
\newblock \emph{Intelligent Data Analysis} 10.6 (2006): 521-538.

\bibitem{barnett}
Barnett, Vic, and Toby Lewis.
\newblock "Outliers in statistical data."
\newblock \emph{Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics, Chichester: Wiley, 1984, 3nd} (1984).

\bibitem{hawkins}
Hawkins, D. M.
\newblock "Identification of outliers."
\newblock \emph{Monographs on Applied Probability and Statistics}, (1980).

\bibitem{leroy}
Leroy, Annick M., and Peter J. Rousseeuw.
\newblock "Robust regression and outlier detection."
\newblock \emph{Wiley Series in Probability and Mathematical Statistics, New York: Wiley}, (1987).

\bibitem{bakar}
Bakar, Zuriana Abu, et al.
\newblock "A comparative study for outlier detection techniques in data mining."
\newblock \emph{Cybernetics and Intelligent Systems, 2006 IEEE Conference on.} IEEE, 2006.

\bibitem{phua}
Phua, Clifton, Damminda Alahakoon, and Vincent Lee.
\newblock "Minority report in fraud detection: classification of skewed data."
\newblock \emph{ACM SIGKDD Explorations Newsletter} 6.1 (2004): 50-59.

\bibitem{joshi}
Joshi, Mahesh V., Ramesh C. Agarwal, and Vipin Kumar.
\newblock "Predicting rare classes: Can boosting make any weak learner strong?."
\newblock \emph{Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.} ACM, 2002.

\bibitem{dasgupta}
Dasgupta, Dipankar, and Fernando Nino.
\newblock "A comparison of negative and positive selection algorithms in novel pattern detection."
\newblock \emph{Systems, Man, and Cybernetics, 2000 IEEE International Conference on.} Vol. 1. IEEE, 2000.

%%\bibitem{singh1}
%%Markou, Markos, and Sameer Singh.
%%\newblock "Novelty detection: a review—part 1: statistical approaches."
%%\newblock \emph{Signal Processing} 83.12 (2003): 2481-2497.
%%
%%\bibitem{singh2}
%%Markou, Markos, and Sameer Singh.
%%\newblock "Novelty detection: a review—part 2:: neural network based approaches."
%%\newblock \emph{Signal Processing} 83.12 (2003): 2499-2521.

%\bibitem{ids1}
%Lazarevic, Aleksandar, et al.
%\newblock "A comparative study of anomaly detection schemes in network intrusion detection."
%\newblock \emph{Proceedings of the third SIAM international conference on data mining.} Vol. 3. Society for Industrial \& Applied, 2003.
%
%\bibitem{ids2}
%Patcha, Animesh, and Jung-Min Park.
%\newblock "An overview of anomaly detection techniques: Existing solutions and latest technological trends."
%\newblock \emph{Computer Networks 51.12 (2007):} 3448-3470.
%
%\bibitem{ids3}
%Lee, Wenke, Salvatore J. Stolfo, and Kui W. Mok
%\newblock "A data mining framework for building intrusion detection models."
%\newblock \emph{Security and Privacy, 1999. Proceedings of the 1999 IEEE Symposium on.} IEEE, 1999.
%
%\bibitem{fraud1}
%Fawcett, Tom, and Foster Provost.
%\newblock "Adaptive fraud detection."
%\newblock \emph{Data mining and knowledge discovery} 1.3 (1997): 291-316.
%
%\bibitem{fraud2}
%Bolton, Richard J., and David J. Hand.
%\newblock "Statistical fraud detection: A review."
%\newblock \emph{Statistical Science} (2002): 235-249.
%
%\bibitem{sensor1}
%Du, Wenliang, Lei Fang, and Ning Peng.
%\newblock "Lad: Localization anomaly detection for wireless sensor networks."
%\newblock \emph{Journal of Parallel and Distributed Computing} 66.7 (2006): 874-886.
%
%\bibitem{sensor2}
%Bhuse, Vijay, and Ajay Gupta.
%\newblock "Anomaly intrusion detection in wireless sensor networks."
%\newblock \emph{Journal of High Speed Networks} 15.1 (2006): 33-51.

\bibitem{song}
Song, Xiuyao, et al.
\newblock "Conditional anomaly detection."
\newblock \emph{Knowledge and Data Engineering, IEEE Transactions on} 19.5 (2007): 631-645.

\bibitem{eskin}
Eskin, Eleazar, et al.
\newblock "A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data." (2002).

%\bibitem{abraham}
%Abraham, Bovas, and Alice Chuang.
%\newblock "Outlier detection and time series modeling."
%\newblock \emph{Technometrics} 31.2 (1989): 241-248.
%
%\bibitem{bianco}
%Bianco, Ana Maria, et al.
%\newblock "Outlier detection in regression models with arima errors using robust estimates."
%\newblock \emph{Journal of Forecasting} 20.8 (2001): 565-579.

\bibitem{meckesheimer}
Basu, Sabyasachi, and Martin Meckesheimer.
\newblock "Automatic outlier detection for time series: an application to sensor data."
\newblock \emph{Knowledge and Information Systems} 11.2 (2007): 137-154.

%\bibitem{perkins1}
%Ma, Junshui, and Simon Perkins.
%\newblock "Time-series novelty detection using one-class support vector machines."
%\newblock \emph{Neural Networks, 2003. Proceedings of the International Joint Conference on.} Vol. 3. IEEE, 2003.

\bibitem{perkins2}
Ma, Junshui, and Simon Perkins.
\newblock "Online novelty detection on temporal sequences."
\newblock \emph{Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining.} ACM, 2003.

%\bibitem{fox}
%Fox, Anthony J.
%\newblock "Outliers in time series."
%\newblock \emph{Journal of the Royal Statistical Society. Series B (Methodological)} (1972): 350-363.

%\bibitem{chuang}
%Abraham, Bovas, and Alice Chuang.
%\newblock "Outlier detection and time series modeling."
%\newblock \emph{Technometrics} 31.2 (1989): 241-248.

%\bibitem{box}
%Abraham, Bovas, and George EP Box.
%\newblock "Bayesian analysis of some outlier problems in time series."
%\newblock \emph{Biometrika} 66.2 (1979): 229-236.

%\bibitem{galeano}
%Galeano, Pedro, Daniel Peña, and Ruey S. Tsay.
%\newblock "Outlier detection in multivariate time series by projection pursuit."
%\newblock \emph{Journal of the American Statistical Association} 101.474 (2006): 654-669.

%\bibitem{zeevi}
%Meir, Assaf J. Zeevi Ron, and Robert J. Adler.
%\newblock "Time series prediction using mixtures of experts."
%\newblock \emph{Advances in Neural Information Processing Systems 9: Proceedings of The 1996 Conference.} Vol. 9. MIT Press, 1997.

\bibitem{faloutsos1}
\em{Christos Faloutsos, M. Ranganathan and Yannis Manolopoulos},
\newblock "Fast Subsequence Matching in Time-Series Databases."
\newblock \emph{Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data.} ACM, 1994

\bibitem{faloutsos2}
Yi, Byoung-Kee, and Christos Faloutsos.
\newblock "Fast time sequence indexing for arbitrary Lp norms."
\newblock \emph{Proceedings of the 26th international conference on very large databases}, 2000.

\bibitem{pong}
\em{Chan, Kin-Pong, and Ada Wai-Chee Fu.}
\newblock "Efficient time series matching by wavelets."
\newblock \emph{Data Engineering, 1999. Proceedings., 15th International Conference on.} IEEE, 1999.

%\bibitem{tsay}
%\em{Tsay, Ruey S., Daniel Peña, and Alan E. Pankratz.}
%\newblock "Outliers in multivariate time series."
%\newblock \emph{Biometrika} 87.4 (2000): 789-804.

%\bibitem{yi}
%Yi, B-K., et al.
%\newblock "Online data mining for co-evolving time sequences."
%\newblock \emph{Data Engineering, 2000. Proceedings. 16th International Conference on}. IEEE, 2000.

\bibitem{ye}
Ye, Nong.
\newblock "A markov chain model of temporal behavior for anomaly detection."
\newblock \emph{Proceedings of the 2000 IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop.} Vol. 166. Oakland: IEEE, 2000.

%\bibitem{hirsh}
%Weiss, Gary M., and Haym Hirsh.
%\newblock "Learning to predict rare events in event sequences."
%\newblock \emph{Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining.} 1998.

%\bibitem{vilalta}
%Vilalta, Ricardo, and Sheng Ma.
%\newblock "Predicting rare events in temporal domains."
%\newblock \emph{Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on.} IEEE, 2002.

%\bibitem{ilgun}
%Ilgun, Koral, Richard A. Kemmerer, and Phillip A. Porras.
%\newblock "State transition analysis: A rule-based intrusion detection approach."
%\newblock \emph{Software Engineering, IEEE Transactions on} 21.3 (1995): 181-199.

\bibitem{blender}
Blender, R., K. Fraedrich, and F. Lunkeit.
\newblock "Identification of cyclone-track regimes in the North Atlantic."
\newblock \emph{Quarterly Journal of the Royal Meteorological Society} 123.539 (1997): 727-741.

\bibitem{sekar1}
Sekar, R., et al.
\newblock "A fast automaton-based method for detecting anomalous program behaviors."
\newblock \emph{Security and Privacy, 2001. S\&P 2001. Proceedings. 2001 IEEE Symposium on.} IEEE, 2001.

\bibitem{sekar2}
Sekar, R., et al.
\newblock "Specification-based anomaly detection: a new approach for detecting network intrusions."
\newblock \emph{Proceedings of the 9th ACM conference on Computer and communications security.} ACM, 2002.

\bibitem{keogh1}
Keogh, Eamonn, et al.
\newblock "Finding the most unusual time series subsequence: algorithms and applications."
\newblock \emph{Knowledge and Information Systems} 11.1 (2007): 1-27.

\bibitem{keogh2}
Keogh, Eamonn, Stefano Lonardi, and Chotirat Ann Ratanamahatana.
\newblock "Towards parameter-free data mining."
\newblock \emph{Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.} ACM, 2004.

\bibitem{keogh3}
Keogh, Eamonn, et al.
\newblock "Locally adaptive dimensionality reduction for indexing large time series databases."
\newblock \emph{ACM SIGMOD Record.} Vol. 30. No. 2. ACM, 2001.

\bibitem{keogh4}
Keogh, Eamonn, et al.
\newblock "Dimensionality reduction for fast similarity search in large time series databases."
\newblock \emph{Knowledge and information Systems} 3.3 (2001): 263-286.

\bibitem{keogh5}
Keogh, Eamonn, and Shruti Kasetty.
\newblock "On the need for time series data mining benchmarks: a survey and empirical demonstration."
\newblock \emph{Data Mining and Knowledge Discovery} 7.4 (2003): 349-371.

%\bibitem{keogh6}
%Chiu, Bill, Eamonn Keogh, and Stefano Lonardi.
%\newblock "Probabilistic discovery of time series motifs."
%\newblock \emph{Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining.} ACM, 2003.

\bibitem{geurts}
Geurts, Pierre.
\newblock "Pattern extraction for time series classification."
\newblock \emph{Principles of Data Mining and Knowledge Discovery} (2001): 115-127.

\bibitem{fu}
Fu, Ada, et al.
\newblock "Finding time series discords based on haar transform."
\newblock \emph{Advanced Data Mining and Applications} (2006): 31-41.

\bibitem{bu}
Bu, Yingyi, et al.
\newblock "Wat: Finding top-k discords in time series database."
\newblock \emph{SDM}, 2007.

\bibitem{yankov}
Yankov, Dragomir, Eamonn Keogh, and Umaa Rebbapragada.
\newblock "Disk aware discord discovery: Finding unusual time series in terabyte sized datasets."
\newblock \emph{Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on.} IEEE, 2007.

%\bibitem{zaki}
%Sequeira, Karlton, and Mohammed Zaki.
%\newblock "ADMIT: anomaly-based data mining for intrusions."
%\newblock \emph{Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.} ACM, 2002.
%
\bibitem{lin}
Lin, Jessica, et al.
\newblock "Approximations to magic: Finding unusual medical time series."
\newblock \emph{Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE Symposium on.} IEEE, 2005.

%\bibitem{hotsax}
%Keogh, Eamonn, Jessica Lin, and Ada Fu.
%\newblock "HOT SAX: Efficiently finding the most unusual time series subsequence."
%\newblock \emph{Data Mining, Fifth IEEE International Conference on.} IEEE, 2005.
%
\bibitem{density_estimation}
Venables, William N., and Brian D. Ripley.
\newblock ch. 5.6 "Density Estimation"
\newblock \emph{Modern applied statistics with S.} Springer, 2002.

%\bibitem{smyth}
%Smyth, Padhraic.
%\newblock "Clustering sequences with hidden Markov models."
%\newblock \emph{Advances in neural information processing systems} (1997): 648-654.

\bibitem{chan}
Chan, Philip K., and Matthew V. Mahoney.
\newblock "Modeling multiple time series for anomaly detection."
\newblock \emph{Data Mining, Fifth IEEE International Conference on.} IEEE, 2005.

%%\bibitem{pca1}
%%Jolliffe, Ian.
%%\newblock \emph{Principal component analysis.} John Wiley \& Sons, Ltd, 2005.
%%
%%\bibitem{pca2}
%%Pearson, Karl.
%%\newblock "LIII. On lines and planes of closest fit to systems of points in space."
%%\newblock \emph{The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science} 2.11 (1901): 559-572.

\bibitem{forrest}
Warrender, Christina, Stephanie Forrest, and Barak Pearlmutter.
\newblock "Detecting intrusions using system calls: Alternative data models."
\newblock \emph{Security and Privacy, 1999. Proceedings of the 1999 IEEE Symposium on.} IEEE, 1999.

\bibitem{sax}
Lin, Jessica, et al.
\newblock "Experiencing SAX: a novel symbolic representation of time series."
\newblock \emph{Data Mining and Knowledge Discovery} 15.2 (2007): 107-144.

%\bibitem{andre}
%André-Jönsson, Henrik, and Dushan Badal.
%\newblock "Using signature files for querying time-series data."
%\newblock \emph{Principles of Data Mining and Knowledge Discovery} (1997): 211-220.
%
%\bibitem{daw}
%Daw, C. Stuart, Charles Edward Andrew Finney, and Eugene R. Tracy.
%\newblock "A review of symbolic analysis of experimental data."
%\newblock \emph{Review of Scientific Instruments} 74.2 (2003): 915-930.
%
%\bibitem{huang}
%Huang, Yun-Wu, and Philip S. Yu.
%\newblock "Adaptive query processing for time-series data."
%\newblock \emph{Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining.} ACM, 1999.Huang, Yun-Wu, and Philip S. Yu.
%
%\bibitem{ultsch}
%Mörchen, Fabian, and Alfred Ultsch.
%\newblock "Optimizing time series discretization for knowledge discovery."
%\newblock \emph{Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining}. ACM, 2005.
%
\bibitem{fabian}
Mörchen, Fabian.
\newblock "Time series knowledge mining."
\newblock \emph{Dissertation}. 2006, Philipps-Universität Marburg.

%\bibitem{salvador}
%Salvador, Stan, Philip Chan, and John Brodie.
%\newblock "Learning states and rules for time series anomaly detection."
%\newblock \emph{Proc. 17th Intl. FLAIRS Conf.} 2004.

%\bibitem{moore}
%Davies, Scott, and Andrew Moore.
%\newblock "Mix-nets: Factored mixtures of gaussians in Bayesian networks with mixed continuous and discrete variables."
%\newblock \emph{Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence.} Morgan Kaufmann Publishers Inc., 2000.

%%\bibitem{ghoting1}
%%Ghoting, Amol, Matthew Eric Otey, and Srinivasan Parthasarathy.
%%\newblock "LOADED: Link-based outlier and anomaly detection in evolving data sets."
%%\newblock \emph{Data Mining, 2004. ICDM'04. Fourth IEEE International Conference on.} IEEE, 2004.
%%
%%\bibitem{ghoting2}
%%Ghoting, Amol, Srinivasan Parthasarathy, and Matthew Eric Otey.
%%\newblock "Fast mining of distance-based outliers in high-dimensional datasets."
%%\newblock \emph{Data Mining and Knowledge Discovery} 16.3 (2008): 349-364.
%%
%%\bibitem{johnson}
%%Johnson, Theodore, Ivy Kwok, and Raymond Ng.
%%\newblock "Fast computation of 2-dimensional depth contours."
%%\newblock \emph{Proc. KDD.} Vol. 1998. 1998.
%
%%\bibitem{spiros}
%%Papadimitriou, Spiros, et al.
%%\newblock "LOCI: Fast outlier detection using the local correlation integral."
%%\newblock \emph{Data Engineering, 2003. Proceedings. 19th International Conference on.} IEEE, 2003.
%
%%\bibitem{breunig}
%%Breunig, Markus M., et al.
%%\newblock "LOF: identifying density-based local outliers."
%%\newblock \emph{ACM Sigmod Record.} Vol. 29. No. 2. ACM, 2000.
%
%\bibitem{jagadish}
%Jagadish, H. V., Nick Koudas, and S. Muthukrishnan.
%\newblock "Mining deviants in a time series database."
%\newblock \emph{Proceedings of the 25th International Conference on Very Large Databases.} 1999.

%\bibitem{ming}
%Li, Ming, et al.
%\newblock "The similarity metric."
%\newblock \emph{Information Theory, IEEE Transactions on} 50.12 (2004): 3250-3264.
%
%%\bibitem{mdl}
%%Grünwald, Peter D. 
%%\newblock \emph{The minimum description length principle.} MIT press, 2007.
%
\bibitem{xiang}
Lee, Wenke, and Dong Xiang.
\newblock "Information-theoretic measures for anomaly detection."
\newblock \emph{Security and Privacy, 2001. S\&P 2001. Proceedings. 2001 IEEE Symposium on.} IEEE, 2001.

%%\bibitem{information-theory}
%%Cover, Thomas M., and Joy A. Thomas.
%%\newblock \emph{Elements of information theory.} Wiley-interscience, 2006.

%\bibitem{immunology}
%Dasgupta, Dipankar, and Stephanie Forrest.
%\newblock "Novelty detection in time series data using ideas from immunology."
%\newblock \emph{Proceedings of the International Conference on Intelligent Systems}. 1996.
%
%\bibitem{immunology2}
%González, Fabio A., and Dipankar Dasgupta.
%\newblock "Anomaly detection using real-valued negative selection."
%\newblock \emph{Genetic Programming and Evolvable Machines} 4.4 (2003): 383-403.

%\bibitem{tarzan}
%Keogh, Eamonn, Stefano Lonardi, and Bill'Yuan-chi Chiu.
%\newblock "Finding surprising patterns in a time series database in linear time and space."
%\newblock \emph{Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.} ACM, 2002.

\bibitem{gopala}
Chen, Scott, and Ponani Gopalakrishnan.
\newblock "Speaker, environment and channel change detection and clustering via the Bayesian Information Criterion."
\newblock \emph{Proc. DARPA Broadcast News Transcription and Understanding Workshop.} 1998.

\bibitem{radke}
Radke, Richard J., et al.
\newblock "Image change detection algorithms: a systematic survey."
\newblock \emph{Image Processing, IEEE Transactions on} 14.3 (2005): 294-307.

\bibitem{clustering}
Jain, Anil K., M. Narasimha Murty, and Patrick J. Flynn.
\newblock "Data clustering: a review."
\newblock \emph{PACM computing surveys (CSUR)} 31.3 (1999): 264-323.

\bibitem{motif}
Sandve, Geir Kjetil, and Finn Drablos.
\newblock "A survey of motif discovery methods in an integrated framework."
\newblock \emph{Biol Direct} 1.11 (2006).

\bibitem{motif2}
Tanaka, Yoshiki, Kazuhisa Iwamoto, and Kuniaki Uehara.
\newblock "Discovery of time-series motif from multi-dimensional data based on mdl principle."
\newblock \emph{Machine Learning} 58.2 (2005): 269-300.

\bibitem{cepstrum}
Kalpakis, Konstantinos, Dhiral Gada, and Vasundhara Puttagunta.
\newblock "Distance measures for effective clustering of ARIMA time-series." 
\newblock \emph{Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on.} IEEE, 2001.

\bibitem{classification}
Tax, David MJ.
\newblock "One-class classification."
\newblock \emph{Dissertation.} University of Delft, 2001.

\bibitem{autocorrelation}
Wang, Changzhou, and X. Sean Wang.
\newblock "Supporting content-based searches on time series via approximation."
\newblock \emph{Scientific and Statistical Database Management, 2000. Proceedings. 12th International Conference on.} IEEE, 2000.

\bibitem{dtw}
Berndt, D., and James Clifford.
\newblock "Using dynamic time warping to find patterns in time series."
\newblock \emph{KDD workshop.} Vol. 10. No. 16. 1994.

\end{thebibliography}