Carnegie Mellon University
15-826 Multimedia Databases and Data Mining
Spring 2007 - C. Faloutsos

Reading List

NOTICE:

Required text

• [Textbook] C. Faloutsos Searching Multimedia Databases by Content, Kluwer Academic Press, 1996. Evaluation draft (internal to CMU - gzipped postscript and pdf)

Recommended text

• [HK] Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.
• [PTVF] William H. Press Saul A. Teukolsky William T. Vetterling Brian P. Flannery Numerical Recipes in C Cambridge University Press, 1992, 2nd Edition. On-line evaluation copy
• Undergraduate DB textbook, for those who took a db class too long ago:
  • Raghu Ramakrishnan, Johannes Gehrke, "Database Management Systems," McGraw-Hill 2002 (3rd ed).

Foils:

In pdf, from the course schedule page.

A. Multimedia Indexing

• Primary key access methods
  • Litwin, W., (1980), Linear Hashing: A New Tool for File and Table Addressing, VLDB, Montreal, Canada, 1980.
  • Textbook, chapters 2-3
• Secondary key and spatial access methods
  • A. Guttman R-Trees: a Dynamic Index Structure for Spatial Searching, Proc. ACM SIGMOD, June 1984, pp. 47-57, Boston, Mass.
  • J. Orenstein, Spatial Query Processing in an Object-Oriented Database System, Proc. ACM SIGMOD, May, 1986, pp. 326-336, Washington D.C..
  • Textbook, chapters 4 and 5.
• Fractals
  • Ibrahim Kamel and Christos Faloutsos, Hilbert R-tree: An improved R-tree using fractals Proc. of VLDB Conference, Santiago, Chile, Sept. 12-15, 1994, pp. 500-509.
  • Christos Faloutsos and Ibrahim Kamel, Beyond Uniformity and Independence: Analysis of R-trees Using the Concept of Fractal Dimension, Proc. ACM SIGACT-SIGMOD-SIGART PODS, May 1994, pp. 4-13, Minneapolis, MN.
• Text and LSI
  • Textbook, chapter 6
  • Peter W. Foltz and Susan T. Dumais, Personalized Information Delivery: an Analysis of Information Filtering Methods, Comm. of ACM (CACM), 35, 12, Dec. 1992, pp. 51-60.
  • SVD: In PTVF ch. 2.6; Textbook Appendix D
• Time sequences
  • Textbook, chapters 7, 8 and 10.
• DSP and image databases
  • Myron Flickner, Harpreet Sawhney, Wayne Niblack, Jon Ashley, Qian Huang, Byron Dom, Monika Gorkani, Jim Hafner, Denis Lee, Dragutin Petkovic, David Steele and Peter Yanker Query by Image and Video Content: the QBIC System IEEE Computer 28, 9, Sep. 1995, pp. 23-32.
  • Journal of Intelligent Inf. Systems, 3, 3/4, pp. 231-262, 1994 An earlier, more technical version of the IEEE Computer '95 paper.
  • FastMap: Textbook chapter 11; Also in: C. Faloutsos and K.I. Lin FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets ACM SIGMOD 95, pp. 163-174.
  • DFT/DCT: In PTVF ch. 12.1, 12.3, 12.4; in Textbook Appendix B.
  • Wavelets: In PTVF ch. 13.10; in Textbook Appendix C
  • Karhunen-Loeve: in Textbook Appendix D.
  • JPEG: Gregory K. Wallace, The JPEG Still Picture Compression Standard, CACM, 34, 4, April 1991, pp. 31-44
  • MPEG: D. Le Gall, MPEG: a Video Compression Standard for Multimedia Applications CACM, 34, 4, April 1991, pp. 46-58
  • Fractal compression: M.F. Barnsley and A.D. Sloan, A Better Way to Compress Images, BYTE, Jan. 1988, pp. 215-223.
  • Textbook, chapter 9

B. Data mining

• Graph mining and social networks:

• Time series forecasting
  • Byong-Kee Yi, Nikolaos D. Sidiropoulos, Theodore Johnson, H.V. Jagadish, Christos Faloutsos and Alex Biliris, Online Data Mining for Co-Evolving Time Sequences, ICDE 2000,  Feb. 2000.

• Statistics background: In PTVF pp. 620-621 and ch. 14.4-14.5;
• AI background / Classification
  • [HK] chapter 7.3
  • Rakesh Agrawal, Sakti Ghosh, Tomasz Imielinski, Bala Iyer and Arun Swami An Interval Classifier for Database Mining Applications VLDB Conf. Proc. Vancouver, BC, Canada, Aug. 1992, pp. 560-573.
  • M. Mehta, R. Agrawal and J. Rissanen, `SLIQ: A Fast Scalable Classifier for Data Mining', Proc. of the Fifth Int'l Conference on Extending Database Technology, Avignon, France, March 1996.
• Data Mining in Databases:
  • Data warehouses, OLAP and DataCubes: [HK], ch. 2.
  • Data reduction: [HK] chapter 3.4
  • Association Rules:
    • [HK] chapter 6
    • Rakesh Agrawal, Tomasz Imielinski and Arun Swami Mining Association Rules Between Sets of Items in Large Databases Proc. ACM SIGMOD, Washington, DC, May 1993, pp. 207-216
  • Cluster analysis: [HK] chapter 8.
• Miscellaneous (ICA, approximate counting)
  • Jia-Yu Pan, Christos Faloutsos, Masafumi Hamamoto and Hiroyuki Kitagawa: AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases, PAKDD, Sydney, Australia, May 2004.
  • Christopher Palmer, Phillip Gibbons and Christos Faloutsos, ANF: A Fast and Scalable Tool for Data Mining in Massive Graphs, KDD 2002, Edmonton, Alberta, Canada, July 2002
  • Efficient and Tunable Similar Set Retrieval, by  Aristides Gionis, Dimitrios Gunopulos and Nikos Koudas, ACM SIGMOD,  Santa Barbara, California, May 21-24, 2001.
  • New sampling-based summary statistics for improving approximate query answers, by Phillip B. Gibbons and Yossi Matias, ACM SIGMOD, pp 331 - 342, Seattle, Washington, 1998.

RECOMMENDED OPTIONAL READING

Additional, optional citations, that may be useful for your project:

Multimedia indexing

• Spatial access methods:
  • N. Beckmann, H.-P. Kriegel, R. Schneider B. Seeger The R*-Tree: an Efficient and Robust Access Method for Points and Rectangles ACM SIGMOD, May 1990, pp. 322-331 Atlantic City, NJ. (Deferred splitting in R-trees)
• Fractals
  • B. Mandelbrot Fractal Geometry of Nature W.H. Freeman, 1977. (The classic book on fractals).
  • Manfred Schroeder, Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise W.H. Freeman and Company, 1991. (An excellent introduction to fractals)

Data mining

• Time sequences
  • George E.P. Box, Gwilym M. Jenkins and Gregory C. Reinsel, Time Series Analysis: Forecasting and Control Prentice Hall, 1994 (3rd Edition). (Time series forecasting - the classic approach. It also has the algorithms for linear predictive coding.)
  • Andreas S. Weigend and Neil A. Gerschenfeld, Time Series Prediction: Forecasting the Future and Understanding the Past Addison Wesley, 1994. (Time series forecasting: non-linear/chaotic approaches)
  • Spiros Papadimitriou, Jimeng Sun and Christos Faloutsos Streaming Pattern Discovery in Multiple Time-Series VLDB 2005, Trondheim, Norway.
• Graph mining:
  • Mining the Web: Discovering Knowledge from Hypertext Data, by Soumen Chakrabarti , Morgan-Kaufmann Publishers
  • Linked: The New Science of Networks by Albert-László Barabási (fun reading - very thought provoking)
• Tom Mitchell, Machine Learning, McGraw Hill, 1997.
• John Ross Quinlan C4.5: Programs for Machine Learning Morgan Kaufmann Publishers Inc., 1993. (Introduction to data mining, with source code)

Last modified: 01/13/2007, by Christos Faloutsos