• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Chap. 10 Mining Object, Spatial, Multimedia, Text, and Web Data

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






Total Views
Views on SlideShare
Embed Views



3 Embeds 5

http://www.slideshare.net 2
http://www.health.medicbd.com 2
http://health.medicbd.com 1



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    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 .