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Dec 5, 2012

Major areas of data mining

Data Mining is a big field now. Each year, several major international conferences are held on the methodology alone (KDD, ICDM, SDM etc).  This does not include specialized conferences on text mining, video mining and so on. On top of these academic conferences, there are several major industry conferences such as Strata and Predictive Analytics World.

We can divide data mining fields based on algorithms or methods:

  • Association Rule Mining (including frequent pattern mining)
  •  Examples are the "beer and diaper" story on shopping in Walmart. It also applies to retail store product selection.
  • Machine Learning  
        o   Supervised  (“predictive modeling”)
§  Neural Network
§  Decision Tree
§  Logistic Regression
§  Support Vector Machine
o   Unsupervised ("clustering")
§  K-means
§  Expectation Maximization (EM)

  • Time series analysis
  • Recommender systems
The areas of data mining can also be separated by data format:
  • Numeric or categorical data – so called “structured” data
  • Text data – Text Mining and Natural language processing
  • Audio data
  • Image data
  • Video data

There are two types of data people work with:

  • Static Data – retrieved from database
  • Stream data
Stream data mining is addressing the problem of continuous incoming data, and very little memory to store all the data. Sometimes the system has to make real-time response.

I hope to get deeper into these areas in future blogs. 

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