課程大綱： Data Mining培訓
Introduction
Data mining as the analysis step of the KDD process ("Knowledge Discovery in Databases")
Subfield of computer science
Discovering patterns in large data sets
Sources of methods
Artificial intelligence
Machine learning
Statistics
Database systems
What is involved?
Database and data management aspects
Data pre-processing
Model and inference considerations
Interestingness metrics
Complexity considerations
Post-processing of discovered structures
Visualization
Online updating
Data mining main tasks
Automatic or semi-automatic analysis of large quantities of data
Extracting previously unknown interesting patterns
groups of data records (cluster analysis)
unusual records (anomaly detection)
dependencies (association rule mining)
Data mining
Anomaly detection (Outlier/change/deviation detection)
Association rule learning (Dependency modeling)
Clustering
Classification
Regression
Summarization
Use and applications
Able Danger
Behavioral analytics
Business analytics
Cross Industry Standard Process for Data Mining
Customer analytics
Data mining in agriculture
Data mining in meteorology
Educational data mining
Human genetic clustering
Inference attack
Java Data Mining
Open-source intelligence
Path analysis (computing)
Reactive business intelligence
Data dredging, data fishing, data snooping
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