Introduction to web mining
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Introduction to Web Mining. Spring 2013. What is data mining?. Data mining is extraction of useful patterns from data sources, e.g., databases, texts, web, images, etc. Patterns must be: valid, novel, potentially useful, understandable. Classic data mining tasks. Classification:

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What is data mining
What is data mining?

  • Data mining is

    • extraction of useful patterns from data sources, e.g., databases, texts, web, images, etc.

  • Patterns must be:

    • valid, novel, potentially useful, understandable


Classic data mining tasks
Classic data mining tasks

  • Classification:

    mining patterns that can classify future (new) data into known classes.

  • Association rule mining

    mining any rule of the form X Y, where X and Y are sets of data items.

  • Clustering

    identifying a set of similarity groups in the data


Classic data mining tasks contd
Classic data mining tasks (contd)

  • Sequential pattern mining:

    A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidence

  • Deviation detection:

    discovering the most significant changes in data

  • Data visualization

CS583, Bing Liu, UIC


Why is data mining important
Why is data mining important?

  • Huge amount of data

    • How to make best use of data?

    • Knowledge discovered from data can be used for competitive advantage.

  • Many interesting things that one wants to find cannot be found using database queries, e.g.,

    “find people likely to buy my products”


WWW

  • Web is an internet-based computer network that allows users of one computer to access information stored on another through the internet.

  • Client-server model, hypertext documents

  • Invented in 1989 by Tim Berners-Lee at CERN with HTTP/HTML

  • Mosaic (1993), Netscape(1994), Internet Explore (1995)

  • Related with Internet (ARPANET, TCP/IP)


Web mining
Web mining

  • traditional data mining

    • data is structured and relational

    • well-defined tables, columns, rows, keys, and constraints.

  • Web data

    • readily available data rich in features and patterns

    • Content/link/usage data


Topic description
Topic Description

  • Introduction to basic data mining: association and sequential mining, classification, clustering

  • Crawling, Web search and information retrieval

  • Social network analysis

  • Structure data extraction, information integration

  • Opinion mining and sentiment analysis

  • Web usage mining


Related fields
Related fields

  • Web mining is an multi-disciplinary field:

    Machine learning

    Statistics

    Databases

    Information retrieval

    Visualization

    Natural language processing

    etc.


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