Selected Papers on Interestingness Measures, Knowledge Discovery and Outlier Mining

• S. Abe  and  T.  Inoue.   Fuzzy  support   vector  machines  for multiclass  problems.In ESANN     2002  Proceedings,    pages  113-118,  2002.

• R.  Agrawal,  T.  Imielinski,   and  A.  Swami.   Mining  association   rules  between sets  of items  in  large  databases.     In  Proceedings    of  the   1993 ACM   SIGMOD Conference, 1993.

• A.  Alink,  C.  M.  Schwiedrzik,   A.  Kohler,  W.  Singer,  and  L.  Muckli.   Stimulus  predictability   reduces  responses  in primary   visual  cortex.    The  Journal    of Neuroscience, 30(8):2960-2966,   February  2010.

• E.L.  Allwein,  RE.   Schapire,  and  Y.  Singer.   Reducing  multiclass  to  binary:   a unifying  approach  for margin  classifiers.   Journal   of Machine    Learning   Research,
  1:113-141,2000.

• C. Anderson.   The  end of theory:   The  data  deluge makes  the  scientific method obsolete. Wired  Magazine,    16(7):108-109,2008.

• P.  Baldi  and  K.  Hornik.   Neural  networks  and  principal   component   analysis: Learning  from  examples  without   local  minima.    Neural   Networks,    2(1):53-581989

• Marc  Benioff.  Data,  data  everywhere:   A special  report  on managing  information.   The  Economist,     February   2010.

• D. E. Berlyne.  Conflict,    arousal,   and  curiosity.    McGraw-Hill  (New York), 1960.

• D.E.  Berlyne.  Aesthetics    and  Psychobiology.     Meredith  Corporation,   New York.,
  1971.

• J.M.  Keller  R   Krishnapuram    L.I.  Kuncheva  J.C.  Bezdek  and  N.R   Pal.   Will the  real  iris data  please  stand  up?   IEEE    Transactions     on  Fuzzy   Systems,    7:3,
  1999.

• B. E. Boser, I. Guyon,  and V. Vapnik.  A training  algorithm  for optimal  margin classifiers.  In  Computational      Learning    Theory,   pages  144-152,  1992.

• H. Boulard  and  Y Kamp.   Auto-association    by multilayer  perceptrons   and  singular  value decomposition.    Biological    Cybernetics,    59:291-294,  1988.

• M.A.J.   Bourassa   and  D.B.  Skillicorn.    Hardening   adversarial   prediction   with anomaly  tracking.   In L. Zhou  M.-Y.  Day C. Yang B. Thuraisingham   D. Zeng, L. Khan  and  H. Chen,  editors,  Proceedings of 2009 IEEE  International   Conference on Intelligence   and Security  Informatics.   IEEE,  June  2009.

• L. Breiman.   Bagging  predictors.    Machine  Learning,  24(2):123U140,  1996.

• L. Breiman.   Random  forests.   Technical  report,  Department   of Statistics, University  of California  Berkeley,  2001.

• C. J. C. Burges.  A tutorial   on support  vector machines  for pattern   recognition.
  Data  Mining  and Knowledge  Discovery,  2:121-167,  1998.

• V. Chandola,  A. Banerjee,  and V. Kumar.  Outlier detection:   a survey. Technical Report  TR 07-017, Department   of Compter  Science and Engineering,  University of Minnesota,   2007.

• V. Chandola,   A. Banerjee,  and V. Kumar.  Anomaly  detection:   A survey.  ACM Comput.  Surv.,  41(3):1-58,   2009.

• C. Chen,  A. Liaw,  and  L. Breiman.   Using random  forest  to  learn  imbalanced data.   Technical  report,   Department   of Statistics,   UC Berkeley, 2004.

• V.  Cherkassky   and  F.  Mulier.     Learning  from  Data:   Concepts,   Theory  and
  Methods.  John  Wiley  & Sons, Inc.,  1998.

• R. Cilibrasi  and  P. Vitanyi.   Clustering  by compression.   IEEE  Transactions  on
  Information    Theory,  51(4):1523-1545,   2005.

• R.  Cilibrasi   and  P.  Vitanyi.     Normalized   web  distance   and  word  similarity.
  CoRR,  abs/0905.4039,   2009.

• R. Cilibrasi,   P. Vitanyi,   and  R. de Wolf.  Algorithmic  clustering  of music.   In
  WEDELMUSIC,    pages  110-117,  2004.

• Thomas  M. Cover and  Joy A. Thomas.   Elements  of information   theory.  Wiley- Interscience,   New York, NY, USA,  1991.

• M.S.  Davis.   That's   interesting!    towards  a phenomenology   of sociology  and  a sociology of phenomenology.   Philosophy  of the Social Sciences,  1:309-344,  1971.

• J.  Dean  and  S.  Ghemawat.     Mapreduce:    simplified  data   processing  on  large clusters. Commun.   ACM,  51(1):107-113,   2008.

• K.I. Diamantaras   and S.Y. Kung.  Principal  Component  Neural Networks:   Theory and Applications.   John  Wiley  & Sons, Inc.,  1996.

• T.  Downs,  I.  Wood,  and  M.  Gallagher.     Empirical   evidence  for  ultrametric structure in multi  layer  perceptron   error  surfaces.    Neural  Processing  Letters,
  16(2):177~186,  2002.

• R.  Duda,   P.  Hart,   and  D.  Stork.    Pattern   Classification.    Wiley-Interscience,
  2001.

• M. H. Dunham.   Data Mining:  Introductory   and Advanced  Topics. Prentice-Hall
  Inc.,  2003.

• L. Ertoz,  M. Steinbach,  and V. Kumar.   A new shared  nearest  neighbor  clustering algorithm  and its applications.   In  Workshop on Clustering High Dimensional Data  and its Applications   at 2nd SIAM  International   Conference  on Data Mining, 2002.

• U.  M.  Fayyad,   G.  Piatetsky-Shapiro,     and  P.  Smyth.    From  data   mining  to knowledge  discovery:   an  overview.    In  Advances   in  knowledge  discovery  and data mining,  pages 1~34. American  Association  for Artificial  Intelligence,  Menlo Park,  CA, USA, 1996.

• S. Forrest,  A.S. Perelson,  and R. Cherukuri  L. Allen. Self-nonself discrimination in a computer.   In IEEE  Symposium   on Research in Security  and Privacy.  IEEE Computer   Society  Press,  1994. Los Alamitos,  CA.

• W.J.  Frawley,  G. Piatetsky-Shapiro,    and  C.J.  Maltheus.   Knowledge  discovery in databases:   An overview.  AI  Magazine,  13(3):57~70,  1992.

• A.A.  Freitas.     Are  we  really  discovering  "interesting"   knowledge  from  data?
  Expert  Update (the BCS-SGAI   Magazine),  9(1):41~47,  October  2006.

• L.  Geng  and  H.  J.  Hamilton.     Interestingness    measures   for  data   mining:   A
  survey.  ACM  Comput.  Surv.,  38(3),  2006.

• Malcolm  Gladwell.  Outliers:  The Story  of Success. Little,  Brown and Company, November  2008.

• T.  Goetz.   Sergey's  search.   Wired Magazine,  18(07):106~112,  July  2010.

• M. Gori and F. Scarselli.  Are multilayer  perceptrons   adequate  for pattern  recognition  and verification?   IEEE  Trans. Pattern  Anal.  Mach. Intell.,  20(11):1121~
  1132, 1998.

• P.M.  Granitto,    P.F.  Verdes,  and  H.A.  Cecatto.     Neural  network  ensembles:
  evaluation   of aggregation   algorithms.    arXiv,  arXiv:cs.AI/0502006vl,    2005.

• M.  Hahsler,   C.  Buchta,   and   K.  Hornik.     Selective  association   rule  generation.   Computational   Statistics,   23(2):303~315,  2008. Also in arXiv:0808.0954vl [cs.DB] 6 March  2008.

• D. Hand,  H. Mannila,  and  P. Smyth.    Principles  of Data  Mining.   MIT  Press, Cambridge,   Massachusetts,   2001.

• D.  J.  Hand.   Data  mining:   Statistics   and  more?     The American   Statistician,
  52(2):112-118,   May 1998.

• S. Hashemi  and T.P.  Trappenberg.    Using svm for classification  in data sets   with ambiguous  data.  In International   Conference  on Information   Systems,  Analysis and Synthesis   (SCI  2002),  2002.

• M.  Hassoun.    Fundamentals   of Artificial   Neural  Networks.    Massachusetts   Institute  of Technology,  1995.

• T. Hastie,  R. Tibshirani,   and J. Friedman.   The Elements  of Statistical  Learning: Data  Mining,  Inference,  and Prediction.   Springer  Series in Statistics.   Springer, second  edition,  2009.

• J.  Hawkins  and  S.  Blakeslee.     On  Intelligence.    Owl  Books  Henry  Holt  and
  Company  LLC,  2005.

• S. Haykin.  Neural  Networks:  A  Comprehensive  Foundation.  Prentice-Hall   Inc., second  edition,  1999.

• Z. He,  X. Xu,  and  S. Deng.   Discovering  cluster-based   local  outliers.    Pattern
  Recognition  Letters,  24(9-10):1641-1650,   2003.

• R. Hecht-Nielsen.  Optimal  source coding,  removable noise elimination,  and natural  coordinate   system  construction   for general  vector  sources  using replicator neural  networks.   In  Proc.  SPIE   Vol. 3077, p.  11-16, Applications   and Science of Artificial  Neural Networks  III,  Steven  K. Rogers; Ed.,  pages 11-16, apr  1997.

• S.   Hettich    and   S.  D.   Bay.   Kdd   cup   1999  data.       UCI   KDD   Archive
  [http://kdd.ics.uci.edu/      /databases/kddcup99/kddcup99.html],         1999.

• T.  Hey, S. Tansley,  and  K. Tolle.   The Fourth  Paradigm:  Data-Intensive   Scientific Discovery.   Microsoft  Research,  Redmond,  Washington,   2009.

• R. J.  Hilderman   and  H. J.  Hamilton.   Knowledge  discovery and  interestingness measures:   A survey.  Technical  report,   University  of Regina,  1999.

• R. J. Hilderman  and H. J. Hamilton.   Measuring  the interestingness  of discovered knowledge:  A principled  approach.   Intell.  Data Anal.,  7(4):347-382,  2003.

• T.  K. Ho.  Random  decision  forests.  In  ICDAR,  pages 278-,  1995.

• T. K. Ho. The random  subspace  method  for constructing  decision forests.  IEEE Trans.  Pattern  Anal.  Mach.  Intell.,  20(8):832-844,   1998.

• C-W.  Hsu and C-J.  Lin. A comparison  of methods  for multiclass  support  vector machines.   IEEE   Transactions   on Neural  Networks,  13:415-425,  2002.

• L. Itti  and P. Baldi.  Bayesian  surprise  attracts   human  attention.   In Proceedings
  Neural  Information   Processing  Systems,   2005.

• S. Johnson.    Where  Good Ideas  Come  From.  Riverhead,  2010.

• M.  A. Kramer.    Nonlinear  principal  component   analysis  using  autoassociative neural  networks.   AIChE   Journal,  37(2):233-243,   1991.

• M. Li and  P. Vitanyi.   An  Introduction   to Kolmogorov  Complexity  and Its  Applications   {Texts  in  Computer  Science}.   Springer,  February   1997.

• B.  Liu.    Web Data  Mining:   Exploring  Hyperlinks,   Contents,   and  Usage Data
  {Data-Centric   Systems   and Applications}.   Springer,  January   2007.

• S. Lu, H. Hu, and  F. Li.  Mining  weighted  association  rules.  Intel.  Data  Anal.,
  5(3):211-225,   2001.

• S. Singh M. Markou.  Novelty detection:   a review part  1: statistical   approaches.
  Signal  Processing,  83(12):2481-2497,   December  2003.

• S. Singh M. Markou.  Novelty detection:   a review part   2: Neural network  based approaches. Signal  Processing,  83(12):2499-2521,   December  2003.

• K. McGarry.  A survey of interestingness   measures  for knowledge discovery.  The
  Knowledge  Engineering  Review,  00:0:1-24,  2005.

• J. L. Melville, E. K. Burke,  and  J. D. Hirst.  Machine learning  in virtual  screening.  Combinatorial   Chemistry  f3 High Throughput  Screening,  12:332-343,  2009.

• P.  M. Murphy  and  M. J.  Pazzani.   Exploring  the  decision  forest:  an empirical investigation of occam's   razor  in  decision  tree  induction.     J.  Artij.   Int.  Res.,
  1(1):257-275,   1993.

• J.  Naisbitt.   Mindset.   HarperCollins,   2006.

• A.  Orriols-Puig,   J.  Casillas,  and  E.  Bernado-Mansilla.    First  approach   toward online evolution  of association  rules with learning classifier systems.  In  GECCO
  '08:  Proceedings  of the  2008  GECCO   conference  companion   on  Genetic  and evolutionary   computation,  pages  2031-2038,  New York, NY, USA, 2008. ACM.

• Y.H. Pao and C.- Y. Shen. Visualization  of pattern  data  through  learning  of non- linear  variance-conserving   dimension-reduction    mapping.    Pattern  Recognition,
  30(10):1705-1717,1997.

• J.  C. Platt,   N. Cristianini,   and  J.  Shawe-Taylor.   Large margin  DAGs for multiclass  classification.   In NIPS,  pages  547-553,  1999.

• J.M.  Puche,  J.M.  Benitez,  and  J.L.  Mantas.   Fuzzy pairwise  multiclass  support vector  machines.   In A. Gelbukh  and  C.A.  Reyes-Garcia,  editors,   Mexican  International   Conference  on Artificial  Intelligence  (MICA I) , volume LNAI, pages 562-571.  Springer-Verlag,   2006.

• A.  Raudys.    Boosting   neural  network  feature  extraction   by reduced  accuracy activation   functions.   Pattern   Recognition,  36:1343-1354,  2003.

• J.  Rissanen.   Modeling  by shortest   data  description.   14:465-471,  1978.

• M. Robnik-Sikonja.   Improving  random  forests.  In J.F.  Boulicaut  et al., editor,
  Machine  Learning,  ECML  2004, 2004.

• D. E. Rumelhart   and  CORPORATE    PDP  Research  Group.  Parallel distributed processing:  explorations  in the microstructure,   vol. 2: psychological and biological models.  MIT  Press,  Cambridge,   MA, USA, 1986.

• C. Saunders,   A. Gammerman,    and  V. Vovk.  Computationally    efficient transductive  machines.  In ALT   '00: Proceedings of the 11th International   Conference on Algorithmic   Learning  Theory,  pages  325-333,  London,  UK, 2000. Springer- Verlag.

• R.  Saunders.     Curious  Design  Agents   and  Artificial   Creativity.    PhD  thesis, Department   of Architectural   and  Design Science, Faculty  of Architecture,   University  of Sydney,  2002.

• J.  Schmidhuber.    What's   interesting?     Technical  Report  IDSIA-35-97,  IDSIA, July  1997.

• J.  Schmidhuber.    Driven  by compression   progress:  A simple  principle  explains essential  aspects  of subjective   beauty,  novelty,  surprise,  interestingness,   attention,   curiosity,   creativity,   art,   science,  music,  jokes.     CoRR,  abs/0812.4360, 2009.

• F.  Y.  Shih  and  K.  Zhang.    Support   vector  machine  networks  for multi class classification.   International   Journal  of Pattern  Recognition  and Artificial  Intelligence, 19(6):775-786,   2005.

• C.  Shirky.   It's  not  information   overload.  it's  filter  failure.   Keynote   Speech, September   2008.

• A. Sierra  and  C. Santa  Cruz.  Global  and  local neural  network  ensembles.   Pattern  Recognition  Letters,  19:651-655,  1998.

• D. Skillicorn. Understanding   Complex  Datasets:  Data  Mining  with Matrix  Decompositions. Chapman   & Hall/CRC   Data  Mining  and  Knowledge  Discovery Series. Chapman   & Hall/CRC   Press,  2007.

• E.  Suzuki.   Data  mining  methods   for  discovering  interesting   exceptions  from an unsupervised   table.   Journal  of Universal  Computer  Science,  12(6):627-653,
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• E.  Suzuki.     Lecture  Notes   in  Computer   Science,   volume  5579/2009,   chapter  Compression-Based    Measures  for Mining  Interesting   Rules,  pages  741-746. Springer  Berlin  /  Heidelberg,  2009.

• P.-N.  Tan,  M. Steinbach,   and  V. Kumar.   Introduction   to Data  Mining,   {First
  Edition}.    Addison-Wesley   Longman  Publishing   Co.,  Inc.,  Boston,  MA, USA,
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• D. Tax  and  R   Duin.   Experiments   with  classifier  combining  rules.   In  Lecture
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• D. Tax and RP.W.   Duin.  Using two-class classifiers for multi class  classification.
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• D. Tax,  M. van  Breukelen,  R.   Duin,  and  J.Kittler.    Combining  multiple  classifiers by averaging  or by multiplying?    Pattern  Recognition,  33:1475-1485,  2000.

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• K.  Yamanishi,   J.-I.  Takeuchi,   G.  J.  Williams,   and  P.  Milnev.   Online  unsupervised  outlier  detection   using  finite  mixtures  with  discounting  learning  algorithms.   In Knowledge  Discovery  and Data  Mining,  pages 320~324,   2000.

• B. Yegnanarayana   and S. P. Kishore.  AANN: an alternative  to GMM for pattern recognition.   Neural  Networks,   15(3):459~469,    2002.