Knowledge%20Discovery%20in%20Database%20(KDD)

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Knowledge Discovery in Database (KDD)
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Knowledge Discovery Process

• The whole process of extraction of implicit,
  previously unknown and potentially useful
  knowledge from a large database
• It includes data selection, cleaning, enrichment,
  coding, data mining, and reporting
• Data Mining is the key stage of Knowledge
  Discovery Process
• The process of finding the desired information
  from large database

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Knowledge Discovery Process

• Example the database of a magazine publisher
  which sells five types of magazines cars,
  houses, sports, music and comics
• Data mining Find interesting customer properties
• What is the profile of a reader of a car
  magazine?
• Is there any correlation between an interest in
  cars and an interest in comics?
• Apply knowledge discovery process

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Data Selection

• Select the information about people who have
  subscribed to a magazine

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Cleaning

• Pollutions Type errors, moving from one place to
  another without notifying change of address,
  people give incorrect information about
  themselves
• Pattern Recognition Algorithms

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Cleaning

• Lack of domain consistency

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Enrichment

• Need extra information about the clients
  consisting of date of birth, income, amount of
  credit, and whether or not an individual owns a
  car or a house

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Enrichment

• The new information need to be easily joined to
  the existing client records
• Extract more knowledge

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Coding

• We select only those records that have enough
  information to be of value (row)
• Project the fields in which we are interested
  (column)

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Coding
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Coding

• Code the information which is too detailed
• Address to region
• Birth date to age
• Divide income by 1000
• Divide credit by 1000
• Convert cars yes-no to 1-0
• Convert purchase date to month numbers starting
  from 1990
• The way in which we code the information will
  determine the type of patterns we find
• Coding has to be performed repeatedly in order to
  get the best results

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Coding

• We are interested in the relationships between
  readers of different magazines
• Perform flattening operation

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Knowledge Discovery Process
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Business-Question-Driven Process
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Steps of a KDD Process

• Learning the Application Domain
• Relevant Prior Knowledge and Goals of Application
• Creating a Target Data Set
• Data Selection
• Data Cleaning and Enrichment
• May Take 60 of Effort
• Data Reduction and Transformation (Coding)
• Find Useful Features, Dimensionality Reduction
• Choosing Functions of Data Mining
• Summarization, Association, Classification,
  Regression, Clustering,
• Choosing the mining algorithm(s)
• Data mining
• Search for Patterns of Interest
• Pattern Evaluation and Knowledge Presentation
• Visualization, Transformation, Removing Redundant
  Patterns, etc.
• Use of Discovered Knowledge

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Exercises 1

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