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Chap. 10 Mining Object, Spatial, Multimedia, Text, and Web Data
 

Chap. 10 Mining Object, Spatial, Multimedia, Text, and Web Data

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    Chap. 10 Mining Object, Spatial, Multimedia, Text, and Web Data Chap. 10 Mining Object, Spatial, Multimedia, Text, and Web Data Presentation Transcript

    • Chap. 10 M ining Object, Spatial, Multimedia, Text, and Web Data Data Mining
    • Mining Complex Types of Data
      • Mining spatial data
      • Mining image data
      • Mining text data
      • Mining the Web
    • Mining Spatial Databases
      • Spatial database
        • Space related data: maps, VLSI layouts, …
        • Topological, distance information organized by spatial indexing structures
      • Spatial data warehousing
        • Issue: different representations & structures
        • Dimensions
          • Nonspatial: 25-30 degree  hot
          • Spatial-to-nonspatial: “New York”  “western provinces”
          • Spatial-to-spatial: equi. temp region  0-5 degree region
        • Measures
          • numerical
          • Spatial: collection of spatial pointers (0-5 degree region)
    • Example: BC Weather Pattern Analysis
      • Input
        • A map with about 3,000 weather probes scattered in B.C.
        • Daily data for temperature, wind velocity, etc.
        • Concept hierarchies for all attributes
      • Output
        • A map that reveals patterns: merged (similar) regions
      • Goals
        • Interactive analysis (drill-down, slice, dice, pivot, roll-up)
        • Fast response time, Minimizing storage space used
      • Challenge
        • A merged region may contain hundreds of “primitive” regions (polygons)
    • Spatial Merge
      • Precomputing: too much storage space
      • On-line merge: very expensive
    • Spatial Association Analysis
      • Spatial association rule: A  B [ s %, c %]
        • A and B are sets of spatial or nonspatial predicates
          • Topological relations: intersects, overlaps, disjoint , etc.
          • Spatial orientations: left_of, west_of, under , etc.
          • Distance information: close_to, within_distance , etc.
        • Example
          • is_a(x, “school”) ^ close_to(x, “sports_center”)
          •  close_to(x, “park”) [7%, 85%]
      • Progressive Refinement
        • First search for rough relationship (e.g. g_close_to for close_to, touch, intersect ) using rough evaluation (e.g. MBR)
        • Then apply only to those objects which have passed the rough test
      • Spatial classification
        • Analyze spatial objects to derive classification schemes, such as decision trees in relevance to spatial properties
        • Example
          • Classify regions into rich vs. poor
          • Properties: containing university, containing highway, near ocean, etc.
      Spatial Classification
      • Constraints- based clustering
        • Selection of relevant objects before clustering
        • Parameters as constraints
          • K-means, density-based: radius, min points
        • Clustering with obstructed distance
      Spatial Cluster Analysis Spatial data with obstacles Clustering without taking obstacles into consideration Mountain River Bridge C1 C2 C3 C4
    • Mining Image Data - Retrieval
      • Description-based retrieval systems
        • Retrieval based on image descriptions, such as keywords, captions, size, etc.
        • Labor-intensive, poor quality
      • Content-based retrieval systems
        • Retrieval based on the image content( features ), such as color histogram, texture, shape, and wavelet transforms
        • Sample-based queries
          • Find all of the images that are similar to the features of given image
        • Feature specification queries
          • Specify or sketch image features like color, texture, or shape, which are translated into a feature vector
    • Mining Image Data - Retrieval C ombining searches Search for “blue sky” (top layout grid is blue) Search for “airplane in blue sky” (top layout grid is blue and keyword = “airplane”)
    • Classification of Image Data
      • Classification
        • Decision tree
          • Based on descriptive features
          • Based on content features
      • Feature extraction
        • Extract features for classification from raw image
        • Various image analysis techniques are required
          • Data transformation, edge detection, etc.
      • Example
        • Classify sky images to recognize galaxies, stars, etc.
        • By using properties obtained from image analysis
    • Classification of Image Data
    • Mining Text Databases
      • Text databases (document databases)
        • Large collections of documents from various sources
          • News articles, research papers, books, e-mail messages, and Web pages
        • Data stored is usually semi-structured
        • Traditional information retrieval techniques become inadequate for the increasingly vast amounts of text data
      • Information retrieval
        • Information is organized into documents
        • Information retrieval problem
          • Locating relevant documents based on user input, such as keywords or example documents
    • Basic Measures for IR
      • Precision : the percentage of retrieved documents that are in fact relevant to the query (i.e., “correct” responses)
      • Recall : the percentage of documents that are relevant to the query and were, in fact, retrieved
    • Keyword-Based Retrieval
      • A document is represented by a set of keywords
        • Retrieval by keyword matching
      • Queries may use expressions of keywords
        • (Car and accessory), (C++ or Java)
      • Major difficulties
        • Synonymy : same meaning but different word
          • Ex> Q: “software”  Doc: about programming, do not have the keyword
        • Polysemy : same word but different meaning
          • Ex> Q: “mining”  Doc: about gold mining, have the keyword
    • Similarity-Based Retrieval
      • A document is represented as a keyword vector
        • Retrieval by similarity computing
      • Basic techniques
        • Stop list – set of words that are frequent but irrelevant
          • Ex> a, the, of, for, with , …
        • Stemming – use a common word stem
          • Ex> drug , drugs, drugged  drug
        • Weighting – count frequency
          • Term frequency, inverse document frequency, …
      • Similarity metrics
        • Measure the closeness of a document to a query
        • Cosine similarity :
    • TF-IDF Weighting
      • TF (Term Frequency)
        • TF= f( t,d ) : h ow many times term t appears in doc d
        • More frequent  more relevant to topic
        • Normalization:
          • Document length varies : relative frequency preferred
      • IDF (Inverse Document Frequency)
        • IDF = 1 + log ( n / k ) : in h ow many documents term t appears
          • n : tot al number of docs
          • k : # docs with term t appearing (the document frequency)
        • Less frequent among documents  more discriminative
      • TF-IDF weighting
        • weight(t, d) = TF(t, d) * IDF(t)
    • Latent Semantic Indexing
      • Reduce the dimension of keyword matrix
        • To resolve the synonym problem and the size problem
        • Use a singular value decomposition (SVD) techniques
      • Example
    • SVD
      • Singular Value Decomposition
        • Decompose the matrix A mn
        • A mn = U mm S mn (V nn ) T
      • Reduce dimension
        • Select largest k singular values
        • A’ mn = U mk S kk (V nk ) T
        • Projection of A into k dimension
        • A’ mn V nk = U mk S kk
        • Computing similarity
        • AA T = USV T (USV T ) T
        • = USV T VS T U T
        • = (US)(US) T
    • SVD
    • Automatic Document Classification
      • Motivation
        • Automatic classification for the tremendous number of on-line text documents (Web pages, e-mails, etc.)
      • A classification problem
        • Training set: Human experts generate a training data set
        • Classification(learning): The system discovers the classification rules
      • Methods
        • Extract keywords and weights from documents
          • Documents are represented as (keyword, weight) pairs
        • Classify training documents into classes
        • Apply classification algorithm
          • Decision tree, Bayesian, neural network, etc.
    • Mining the World-Wide Web
      • WWW provides rich sources for data mining
        • Contents information
        • Hyperlink information
        • Usage information
      • Challenges
        • Too huge for effective data warehousing and data mining
        • Too complex and heterogeneous
        • Growing and changing very rapidly
    • Web Search Engines
      • Index-based
        • Search the Web, collect Web pages, index Web pages, and build and store huge keyword-based indices
        • Locate sets of Web pages containing certain keywords
      • Deficiencies
        • A topic of any breadth may easily contain hundreds of thousands of documents
        • Many documents that are highly relevant to a topic may not contain keywords defining them (synonymy, polysemy)
    • Web Contents Mining - Classification
      • Web page/site classification
        • Assign a class label to each web page from a set of predefined topic categories
        • Based on a set of examples of preclassified documents
      • Example
        • Use Yahoo!'s taxonomy and its associated documents as training and test sets
        • Derive a Web document classification model
        • Use the model to classify new Web documents by assigning categories from the same taxonomy
      • Methods
        • Keyword-based classification, use of hyperlink information, statistical models, …
    • Web Structure Mining
      • Finding authoritative Web pages
        • Retrieving pages that are not only relevant, but also of high quality, or authoritative on the topic
      • Hyperlinks can infer the notion of authority
        • A hyperlink pointing to another Web page, this can be considered as the author's endorsement of the other page
      • Problems
        • Not every hyperlink represents an endorsement
        • One authority will seldom point to its rival authority
        • Authoritative pages are seldom particularly descriptive
      • Hub
        • Set of Web pages that provides collections of links to authorities
    • HITS (Hyperlink-Induced Topic Search)
      • Method
        • Use an index-based search engine to form the root set
        • Expand the root set into a base set
          • Include all of the pages that the root-set pages link to, and all of the pages that link to a page in the root set
        • Apply weight-propagation
          • Determines numerical estimates of hub and authority weights
        • Output a list of the pages
          • Large hub weights, large authority weights for the given search topic
      • Systems based on the HITS algorithm
        • Clever, Google
          • Achieve better quality search results than AltaVista, Yahoo!
    • Web Usage Mining
      • Mining Web log records
        • Discover user access patterns
        • Typical Web log entry - URL requested, the IP address from which the request originated, timestamp, etc.
      • OLAP on the Weblog database
        • Find the top N users, top N accessed Web pages, most frequently accessed time periods, etc.
      • Data mining on Weblog records
        • Find association patterns, sequential patterns, and trends of Web accessing
    • Web Usage Mining
      • Applications
        • Target potential customers for electronic commerce
        • Identify potential prime advertisement locations
        • Enhance the quality and delivery of Internet information services to the end user
        • Improve Web server system performance
          • Web caching, Web page prefetching, and Web page swapping
    • References
      • H. Miller and J. Han (eds.), Geographic Data Mining and Knowledge Discovery, Taylor and Francis, 2001.
      • Ester M., Frommelt A., Kriegel H.-P., Sander J.: Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support , Data Mining and Knowledge Discovery, 4: 193-216, 2000.
      • J. Han, M. Kamber, and A. K. H. Tung, "Spatial Clustering Methods in Data Mining: A Survey", in H. Miller and J. Han (eds.), Geographic Data Mining and Knowledge Discovery, Taylor and Francis, 2000.
      • Y. Bedard, T. Merrett, and J. Han, "Fundamentals of Geospatial Data Warehousing for Geographic Knowledge Discovery", in H. Miller and J. Han (eds.), Geographic Data Mining and Knowledge Discovery, Taylor and Francis, 2000
      • K. Koperski and J. Han. Discovery of spatial association rules in geographic information databases. SSD'95.
      • Shashi Shekhar and Sanjay Chawla, Spatial Databases: A Tour , Prentice Hall, 2003 (ISBN 013-017480-7). Chapter 7. : Introduction to Spatial Data Mining
      • X. Li, J. Han, and S. Kim, Motion-Alert: Automatic Anomaly Detection in Massive Moving Objects”, IEEE Int. Conf. on Intelligence and Security Informatics (ISI'06).
      • Fabrizio Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, Vol. 34, No.1, March 2002
      • Soumen Chakrabarti, “Data mining for hypertext: A tutorial survey”, ACM SIGKDD Explorations, 2000.
      • Cleverdon, “Optimizing convenient online access to bibliographic databases”, Information Survey, Use4, 1, 37-47, 1984
      • Yiming Yang, “An evaluation of statistical approaches to text categorization”, Journal of Information Retrieval, 1:67-88, 1999.
      • Yiming Yang and Xin Liu “A re-examination of text categorization methods”. Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99, pp 42--49), 1999.
      • S. Chakrabarti, “Mining the Web: Statistical Analysis of Hypertext and Semi-Structured Data” , Morgan Kaufmann, 2002.
    • References
      • G. Miller, R. Beckwith, C. FellBaum, D. Gross, K. Miller, and R. Tengi. Five papers on WordNet. Princeton University, August 1993.
      • M. Hearst, Untangling Text Data Mining , ACL’99, invited paper. R. Sproat, Introduction to Computational Linguistics , LING 306, UIUC, Fall 2003.
      • A Road Map to Text Mining and Web Mining, University of Texas resource page. http://www.cs.utexas.edu/users/pebronia/text-mining/
      • Deng Cai, Shipeng Yu, Ji-Rong Wen and Wei-Ying Ma, “Extracting Content Structure for Web Pages based on Visual Representation”, The Fifth Asia Pacific Web Conference, 2003.
      • Deng Cai, Shipeng Yu, Ji-Rong Wen and Wei-Ying Ma, “VIPS: a Vision-based Page Segmentation Algorithm”, Microsoft Technical Report (MSR-TR-2003-79), 2003.
      • Shipeng Yu, Deng Cai, Ji-Rong Wen and Wei-Ying Ma, “Improving Pseudo-Relevance Feedback in Web Information Retrieval Using Web Page Segmentation”, 12th International World Wide Web Conference (WWW2003), May 2003.
      • Ruihua Song, Haifeng Liu, Ji-Rong Wen and Wei-Ying Ma, “Learning Block Importance Models for Web Pages”, 13th International World Wide Web Conference (WWW2004), May 2004.
      • Deng Cai, Shipeng Yu, Ji-Rong Wen and Wei-Ying Ma, “Block-based Web Search”, SIGIR 2004, July 2004 .
      • Deng Cai, Xiaofei He, Ji-Rong Wen and Wei-Ying Ma, “Block-Level Link Analysis”, SIGIR 2004, July 2004 .
      • Deng Cai, Xiaofei He, Wei-Ying Ma, Ji-Rong Wen and Hong-Jiang Zhang, “Organizing WWW Images Based on The Analysis of Page Layout and Web Link Structure”, The IEEE International Conference on Multimedia and EXPO (ICME'2004) , June 2004
      • Deng Cai, Xiaofei He, Zhiwei Li, Wei-Ying Ma and Ji-Rong Wen, “Hierarchical Clustering of WWW Image Search Results Using Visual, Textual and Link Analysis”,12th ACM International Conference on Multimedia, Oct. 2004 .