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Data Mining Lecture 1 (What is data mining? (Data mining can help to:…

- - - - Sources:
        
        Business
        
        Science
        
        Medicine
        
        Economics
        
        Geography
        
        Environment
        
        Sports
        
        Activity data
        
        Surveillance
      - Potentially valuable resource
      - \[ \text{Data }\rightarrow\text{ recorded fact} \]
      - \[ \text{Information }\rightarrow\text{ patterns underlying the data} \]
      - Raw data is useless
        
        We need techniques to automatically extract information from it
- - - - Numeric
      - Binary
      - Categorical (describe types)
    - - Nominal
        
        ID numbers
        
        Eye colour
        
        ZIP codes
      - Ordinal
        
        Rankings
        
        Grades
        
        Height
        
        Tall
        
        Medium
        
        Short
      - Interval
        
        Calendar dates
        
        Temperatures in Celsius or Fahrenheit
      - Ratio
        
        Temperature in Kelvin
        
        Length
        
        Time
        
        Counts
    - - Goal: to build a model for predicting one variable of interest
      - Tasks:
        
        Classification
        
        Lecture 2-3
        
        Regression
        
        Lecture 5
        
        Anomaly detection
        
        Recommender systems
        
        Lecture 9
      - Given a set of input attributes predict the label
        
        Classification
        
        Is this email 'spam' or 'ham'?
        
        Regression
        
        What is the price of this house?
      - Recommender system
        
        Predict the 'rating' or 'preference' that a user would give to an item
        
        Music
        
        Books
        
        Movies
        
        Etc.
    - - Goal: the user has a pattern of interest and wishes to find similar patterns in the data-set
      - Tasks: search
        
        Google web search
    - - The true outcome is known in the data-set (label)
      - We can "direct" (supervise) model building process by taking into account the label
      - We know if the prediction is right or wrong (error or reward can be measured)
  - - - Goal: to explore the data without clear ideas of what we are looking for
      - Tasks: interactive and visual analysis
        
        Lecture 4
    - - Goal: to build a model to describe and summarise data
      - Tasks
        
        Clustering
        
        Lecture 6
        
        Association analysis
        
        Lecture 7
      - Examples
        
        Customer segmentation
        
        Clustering
        
        Market basket analysis, frequently bought together
        
        Association analysis
    - - The model is supposed to find patterns/structure in data
      - We don't know "the true" structure behind
      - It is difficult to judge if the model is right or wrong
- - - - To make large data mining projects run more efficiently, be cheaper, more reliable, and more manageable
    - - Business Understanding
        
        Determine Business Objectives
        
        Background
        
        Business Objectives
        
        Business Success Criteria
        
        Access Situation
        
        Inventory of Resources
        
        Requirements, Assumptions, and Constraints
        
        Risk and Contingencies
        
        Terminology
        
        Cost and Benefits
        
        Determine Data Mining Goals
        
        Data Mining Goals
        
        Data Mining Success Criteria
        
        Produce Project Plan
        
        Project Plan
        
        Initial Assessment of Tools and Techniques
      - Data Understanding
        
        Collect Initial Data
        
        Initial Data Collection Report
        
        Describe Data
        
        Data Description Report
        
        Explore Data
        
        Data Exploration Report
        
        Verify Data Quality
      - Data Preparation
        
        Select Data
        
        Rationale for Inclusion/Exclusion
        
        Clean Data
        
        Data Cleaning Report
        
        Construct Data
        
        Derived Attributes
        
        Generated Reports
        
        Integrate Data
        
        Merged Data
        
        Format Data
        
        Reformatted Data
        
        Data-set
        
        Data-set Description
      - Modelling
        
        Select Modelling Techniques
        
        Modelling Technique
        
        Modelling Assumptions
        
        Generate Test Design
        
        Test Design
        
        Build Model
        
        Parameter Settings
        
        Models
        
        Model Descriptions
        
        Assess Model
        
        Model Assessment
        
        Revised Parameter Settings
      - Evaluation
        
        Evaluate Results
        
        Assessment of Data
        Mining Results w.r.t
        Business Success Criteria
        
        Approved Models
        
        Review Process
        
        Review of Process
        
        Determine Next Steps
        
        List of Possible Actions
        
        Decisions
      - Deployment
        
        Plan Deployment
        
        Deployment Plan
        
        Plan Monitoring and Maintenance
        
        Monitoring and Maintenance Plan
        
        Produce Final Report
        
        Final Report
        
        Final Presentation
        
        Review Project
        
        Experience Documentation