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(Text Mining Terminology (Synonyms (and polysemes), Tokenizing, Stop words…

- - - - How many times an individual fill a shopping cart but fail to complete transaction
      - Which web site products are best and worst sellers
      - How often a Web site is visited
  - - - 2) Identify unwanted log file entries
        
        A single user page request oftentimes generates multiple log file entries from several types of servers (e.g. image servers)
        
        Must have a technique to identify unwanted log file entries so that they do not become part of the session file
      - 3) Data transformation
        
        New attributes can be added to user session
        records to improve prediction of outcome
        
        Examples
        
        Average purchase amount of repeat customers
        
        Time of most recent transaction
      - 1) Differentiate individual user sessions
        
        May be able to differentiate different user sessions by combining host addresses with the referring page
        
        Best able to differentiate user sessions if cookies (data files) are allowed to be placed on users’ computers to contain session information
        
        Host addresses are not useful because multiple
        users may access a site from the same host
  - - - Association Rule Mining
      - Unsupervised clustering
        
        Usage profiles for clusters
        
        Form clusters of similar user session instances to personalize pages viewed by web site users
  - - - Personalise web pages for customers
      - Determine products for sale at a web site that tend to
        be purchased or viewed together
      - Improve usability of web site – decrease the average
        no. of pages visited by a customer before a purchase
        transaction
  - - - Web-page Usability Optimization
        
        Adapt the indexing structure of a Web site to better reflect the paths followed by typical users and the changes in needs of users over time.
      - Personalization of web pages based on user profiles:
        
        Implement strategy to personalize web pages
        
        • Manual : force users to register at web site
        
        Data mining to automate personalisation based on actual user behavior
      - Targeted Marketing Communications
        
        Send e-mail to promote products of likely interest to a select group of registered customers
        
        Modify content of a Web site by grouping products likely to be purchased together
        
        Set up online advertising promotions
- - - - The development of useful information from the
        links included in the Web documents
      - Web pages include hyperlinks: Authoritative pages. Hubs: List of recommended links to authoritative pages. Hyperlink-induced topic search (HITS) algorithm
      - The unified resource locator (URL) links contained in the Web pages
    - - Extraction of information from clickstream analysis of web server logs generated through Web page visits and transactions 1) data stored in server access logs, referrer logs, agent logs, and client-side cookies. 2)user characteristics and usage profiles. 3)metadata, such as page attributes, content attributes, and usage data
      - The detailed description of a Web site's visits (sequence of clicks by sessions)
      - Clickstream data
      - Clickstream analysis of web server logs
    - - The extraction of useful information from Web pages (textual content)
        
        Data collection via web crawlers eg Googlebot
        
        Used for competitive intelligence, information/news/opinion collection, sentiment analysis, and automated data collection
      - Unstructured textual content of the Web pages (usually in HTML format)
  - - - Determine the lifetime value of clients
      - Design cross-marketing strategies across products.
      - Target electronic ads and coupons at user groups based on user access patterns
      - Predict user behavior based on previously learned rules and users' profiles
      - Present dynamic information to users based on their interests and profiles
- - - - Should all terms be included? Stop words, include words. Synonyms, homonyms. Stemming : reduction of words to their roots eg modeling, modeled, models, etc.
      - What is the best representation of the indices (values in cells)? Row counts; binary frequencies; log frequencies;
      - How can we reduce the dimensionality of the TDM? Manual - a domain expert goes through it. Eliminate terms with very few occurrences in very few
        documents (Filtering)