Knowledge Discovery in Database (KDD) and Data Mining

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Knowledge Discovery in Database (KDD) and Data
Mining

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• OLAP (Online Analytical Process)
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KDD??

• Is the non-trivial extraction of implicit ,
  previous unknown, and potentially useful
  information from database.(Ester, 1997)
• Including regularity, rules, and clusters
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• ?????machine learning?AI?Database??????????pattern
  recognition ???

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KDD???(Fayyad, 1997)

• Selection
• Pre-processing
• Transformation
• Data Mining(application of speific algorithm)
• Description and Visualization
• Evaluation and Interpretation

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Data Mining

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• Is the process of exploration and analysis, by
  automatic or semi-automatic means, of large
  quantities of data in order to discover
  meaningful patterns or rules.

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Data Mining (contd)

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• Classification(By pre-defined class)
• Regression
• Clustering
• Summarization
• Association
• Sequence analysis
• Prediction

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Data Mining (contd)

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• Researchers and practitioners should ensure that
  the potential contributions of data mining are
  not overstated and that users understand the true
  nature of those contributions along with their
  limitations.
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Data Warehouse(Inmon, 1997)

• There is a symbiotic relationship between the
  activities of KDD and the data warehousing-the
  architectural foundation of decision support
  systems.
• offers data that is clean, integrated, and
  complete
• Data mining can be done where there is no data
  warehouse, but the data warehouse greatly
  improves the chances of success in data mining.