Data Intelligence and Mining

1
Data Intelligence and Mining

• CSCI 317A

2
Announcements

• Teams
• Topics
• Make sure you have a specific time every cycle
  that you can meet in
• Quiz 1 on Thursday
• First 20 minutes or so
• HW 1 on Monday (Individual)
• Clearly typed and organized report
• Summer Research Opportunity
• http//www.bsi.umn.edu/
• June 4 through August 10, 2007
• Stipend of 5,500
• junior or a senior undergraduate student
• Deadline February 28 (2 recommendation letters)

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Recap

• What is classification?
• Purpose?
• How is it done?
• Application
• What is outlier/deviation/anomaly detection?
• Application

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Association Rule Mining

• Given a set of records each containing a number
  of items
• Different type of data
• Produce dependency rules which will predict
  occurrence of an item based on occurrences of
  other items
• MBR
• Supermarket shelf management or Marketing and
  Sales Promotions
• Wal-Mart!
• Efficiency
• 50 items
• (250 1 Peta or a million billion)

Rules Discovered Milk --gt Coke
Diaper, Milk --gt Beer
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Association Rule Discovery Application 1

• Marketing and Sales Promotion
• Let the rule discovered be
• Dip --gt Tortilla Chips
• Chips as consequent gt Can be used to determine
  what should be done to boost its sales
• Sales on Dip!
• Could be brand-specific
• Dip in the antecedent gt Can be used to check
  which products would be affected if the store
  discontinues selling a certain Dip brand

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Association Rule Discovery Application 2

• Supermarket shelf management
• Goal To identify items that are bought together
  by sufficiently many customers.
• Approach Process the point-of-sale data
  collected with barcode scanners to find
  dependencies among items
• Dip --gt Tortilla Chips
• Expensive brands
• Splenda vs. Sweet N Low
• WebPage X --gt WebPage Y
• Web Structure Mining

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Association Rule Discovery

• Among Genes G1 ? G2
• G1s expression depend upon G2s
• Might be involved in the same biological systems
  or pathways
• Finer clusters with directionality

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Conferences and Journals on Data Mining

• KDD Conferences
• ACM SIGKDD Int. Conf. on Knowledge Discovery in
  Databases and Data Mining (KDD)
• SIAM Data Mining Conf. (SDM)
• (IEEE) Int. Conf. on Data Mining (ICDM)
• Conf. on Principles and practices of Knowledge
  Discovery and Data Mining (PKDD)
• Pacific-Asia Conf. on Knowledge Discovery and
  Data Mining (PAKDD)
• ACM Symposium on Applied Computing (SAC) Bio
  and DM tacks

• Other related conferences
• ACM SIGMOD
• VLDB
• (IEEE) ICDE
• WWW, SIGIR
• ICML, CVPR, NIPS
• Journals
• Data Mining and Knowledge Discovery (DAMI or
  DMKD)
• IEEE Trans. On Knowledge and Data Eng. (TKDE)
• KDD Explorations
• ACM Trans. on KDD

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Recommended Reference Books

• S. Chakrabarti. Mining the Web Statistical
  Analysis of Hypertex and Semi-Structured Data.
  Morgan Kaufmann, 2002
• R. O. Duda, P. E. Hart, and D. G. Stork, Pattern
  Classification, 2ed., Wiley-Interscience, 2000
• T. Dasu and T. Johnson. Exploratory Data Mining
  and Data Cleaning. John Wiley Sons, 2003
• U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and
  R. Uthurusamy. Advances in Knowledge Discovery
  and Data Mining. AAAI/MIT Press, 1996
• U. Fayyad, G. Grinstein, and A. Wierse,
  Information Visualization in Data Mining and
  Knowledge Discovery, Morgan Kaufmann, 2001
• J. Han and M. Kamber. Data Mining Concepts and
  Techniques. Morgan Kaufmann, 2nd ed., 2006
• D. J. Hand, H. Mannila, and P. Smyth, Principles
  of Data Mining, MIT Press, 2001
• T. Hastie, R. Tibshirani, and J. Friedman, The
  Elements of Statistical Learning Data Mining,
  Inference, and Prediction, Springer-Verlag, 2001
• T. M. Mitchell, Machine Learning, McGraw Hill,
  1997
• G. Piatetsky-Shapiro and W. J. Frawley. Knowledge
  Discovery in Databases. AAAI/MIT Press, 1991
• P.-N. Tan, M. Steinbach and V. Kumar,
  Introduction to Data Mining, Wiley, 2005
• S. M. Weiss and N. Indurkhya, Predictive Data
  Mining, Morgan Kaufmann, 1998
• I. H. Witten and E. Frank, Data Mining
  Practical Machine Learning Tools and Techniques
  with Java Implementations, Morgan Kaufmann, 2nd
  ed. 2005

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Additional References

• Barnett, V. and T. Lewis (1994) Outliers in
  Statistical Data. New York Wiley
• Afifi, A.A., and Azen, S.P. (1972), Statistical
  analysis a computer oriented sapproach, Academic
  Press, New York.
• Huber, P.J. (1985), Projection pursuit, The
  Annals of Statistics, 13(2), 435-475.
• David J. Marchette and Jeffrey L. Solka Using
  data images for outlier detection  Computational
  Statistics Data Analysis, Volume 43, Issue 4,
  28 August 2003, Pages 541-552
• Joliffe, I.T. (1986) Principal Component
  Analysis, Springer-Verlag, New York.
• Robust Regression and Outlier Detection (Wiley
  Series in Probability and Statistics) by Peter J.
  Rousseeuw, Annick M. Leroy , Wiley-Interscience
  (September 19, 2003)
• J. Han and M. Kamber. Data Mining Concepts and
  Techniques. Morgan Kaufmann, 2nd ed., 2006
• P. Tan, M. Steinbach and V. Kumar, Introduction
  to Data Mining, Wiley, 2005