Introduction to Data Mining and Knowledge Discovery

четверг, 10 декабря 2009, Александр Краковецкий

Content:

Introduction

  • Data mining: In brief
  • Data mining: What it can’t do
  • Data mining and data warehousing
  • Data mining and OLAP
  • Data mining, machine learning and statistics
  • Data mining and hardware/software trends
  • Data mining applications
  • Successful data mining

Data Description for Data Mining

  • Summaries and visualization
  • Clustering
  • Link analysis

Predictive Data Mining

  • A hierarchy of choices
  • Some terminology
  • Classification
  • Regression
  • Time series

Data Mining Models and Algorithms

  • Neural networks
  • Decision trees
  • Multivariate Adaptive Regression Splines (MARS)
  • Rule induction
  • K-nearest neighbor and memory-based reasoning (MBR)
  • Logistic regression
  • Discriminant analysis
  • Generalized Additive Models (GAM)
  • Boosting
  • Genetic algorithms
  • The Data Mining Process
  • Process Models
  • The Two Crows Process Model

Selecting Data Mining Products

  • Categories
  • Basic capabilities

Summary

PDF file is attached to the post.

Content (media) Data mining Data warehouse Online analytical processing Machine learning Statistics Personal computer hardware Computer software Application software Data URI scheme Summary Visualization (computer graphics) Hyperlink Analysis Prediction Hierarchy Choice Terminology Classification (machine learning) Regression analysis Time series Algorithm Neural network Decision tree Mars K-nearest neighbor algorithm Memory Reasoning Logistic regression Linear discriminant analysis Generalization Generalized additive model Boosting Genetic algorithm Process control Process modeling Visual Basic Portable Document Format Post-production

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