Carnegie Mellon University
15-826: Multimedia and Data Mining
Fall 2024 - Christos Faloutsos
Reading list

NOTICE:

Required texts

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

• [Graph-Textbook] Deepayan Chakrabarti and Christos Faloutsos Graph Mining: Laws, Tools and Case Studies, Morgan Claypool, 2012. Evaluation draft (internal to CMU - pdf).
  

Recommended texts

• [HKP] Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011 (amazon link).
  
• [PTVF] William H. Press Saul A. Teukolsky William T. Vetterling Brian P. Flannery Numerical Recipes in C Cambridge University Press, 1992, 2nd Edition, free on-line. Latest is 3rd edition, 2007.
• 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

• Secondary key and spatial access methods
  • Jon Louis Bentley, Multidimensional binary search trees used for associative searching, Comm. of the ACM  (CACM), Volume 18 ,  Issue 9, pp. 509-517,  (September 1975)
  • 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.
  • MM-Textbook, chapters 4 and 5.
• Fractals
  
  • 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.
  • Bernd-Uwe Pagel, Flip Korn and Christos Faloutsos, Deflating the Dimensionality Curse using Multiple Fractal Dimensions, ICDE 2000, San Diego, CA, Feb. 2000.
    
  • Power laws, lognormals etc: M. E. J. Newman, Power laws, Pareto distributions and Zipf's law Contemporary Physics 46, 323-351 (2005) (local pdf copy)
• Text and LSI
  • MM-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; MM-Textbook Appendix D
  • PageRank: Sergey Brin, Lawrence Page The Anatomy of a Large-Scale Hypertextual Web Search Engine (1998) (local pdf)
  • HITS: Jon M. Kleinberg Authoritative Sources in a Hyperlinked Environment JACM, 46,5 (1999) (local pdf)
  • Tensors survey: Papalexakis, Faloutsos, Sidiropoulos Tensor for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms ACM Trans. on Intelligent Systems and Technology, 8,2, Oct. 2016. (local copy)
  • Tensors: [Graph-Textbook] Ch.16.
    
• Time sequences
  • MM-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.
  • FastMap: MM-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 MM-Textbook Appendix B.
  • Wavelets: In PTVF ch. 13.10; in MM-Textbook Appendix C
  • Karhunen-Loeve: in MM-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
  • Laurens van der Maaten, Geoffrey Hinton, Visualizing Data using t-SNE, JMLR 9(86):2579−2605, 2008. local copy.
  • Leland McInnes, John Healy, James Melville, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arxiv, 2018 local copy.
    
  • Fractal compression: M.F. Barnsley and A.D. Sloan, A Better Way to Compress Images, BYTE, Jan. 1988, pp. 215-223.
  • MM-Textbook, chapter 9

B. Data mining

• Graph mining and social networks:
  • Michalis Faloutsos, Petros Faloutsos and Christos Faloutsos, On Power-Law Relationships of the Internet Topology, SIGCOMM 1999.
  • R. Albert, H. Jeong, and A.-L. Barabási, Diameter of the World Wide Web Nature, 401, 130-131 (1999).
  • Réka Albert and Albert-László Barabási Statistical mechanics of complex networks, Reviews of Modern Physics, 74, 47 (2002).
    
  •  Haveliwala, Taher H. (2003) Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search. Technical Report. Stanford InfoLab. (Extended version of the WWW2002 paper on Topic-Sensitive PageRank.)
  •  D. Chakrabarti, S. Papadimitriou, D. Modha and C. Faloutsos, Fully Automatic Cross-Associations, in KDD 2004 (pages 79-88), Washington, USA
  • Hanghang Tong, Christos Faloutsos   Center-Piece Subgraphs: Problem Definition and Fast Solutions, KDD 2006, Philadelphia, PA
    
  • Jure Leskovec, Jon Kleinberg, Christos Faloutsos Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations, KDD 2005, Chicago, IL, USA, 2005.
  • J. Leskovec, D. Chakrabarti, J. Kleinberg, and C. Faloutsos, Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication, in PKDD 2005, Porto, Portugal
  • Danai Koutra, Tai-You Ke, U. Kang, Duen Horng Chau, Hsing-Kuo Kenneth Pao, and Christos Faloutsos. Unifying guilt-by-association approaches: theorems and fast algorithms. ECML/PKDD'11, Athens, Greece.
  • [Graph-Textbook], Ch.18: virus propagation
  • [Graph-Textbook], Ch.19: Case studies: Random walk with restarts, image captioning.
    
• Time series forecasting
  • Chungmin Melvin Chen and Nick Roussopoulos, Adaptive Selectivity Estimation Using Query Feedbacks, SIGMOD 1994.
    
  • 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.
  • Steven L. Brunton, Joshua L. Proctor, and J. Nathan KutzAuthors, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, PNAS 113 (15) 3932-3937, March 28, 2016, https://doi.org/10.1073/pnas.1517384113 ( local copy).
    
• Statistics background: In PTVF pp. 620-621 and ch. 14.4-14.5;
• AI background / Classification
  • [HKP] chapter 8.1-2.
    
  • 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:
  • Association Rules:
    • [HKP] 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
    • Ahmed El-Kishky, Yanglei Song, Chi Wang, Clare R. Voss, Jiawei Han: Scalable Topical Phrase Mining from Text Corpora. Proc. VLDB Endow. 8(3): 305-316 (2014)
      
  • Cluster analysis: [HKP] chapter 10.
    
• 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.
  • Ahmed Metwally, Divyakant Agrawal, Amr El Abbadi: Efficient Computation of Frequent and Top-k Elements in Data Streams. ICDT 2005: 398-412
    

RECOMMENDED OPTIONAL READING

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

Multimedia indexing

• 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)
• 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.

Last modified: Aug. 27, 2024, by Christos Faloutsos