This blog is the index/syllabus for the blog series on Data Mining, which I will publish in 5 parts, and I will mostly use my university notes for it.

Part 1: Introduction to Data Mining

  • Tasks
  • Issues
  • Decision Support System
  • Dimentional Modelling
  • Data Warehousing
  • OLAP & its tools
  • OLTP

Part 2: Mining Techniques & Clasification

  • Introduction
  • Statistical Perspective of Data Mining
  • Decision Tree
  • Neural Networks
  • Genetic Algorithms
  • Issues in Classification
  • Statistic Based Algorithm(Regression)
  • Distance Based Algorithm
  • Decision Tree Based Algorithm(C4.5)
  • Neural network Based Algorithm(Propogation)

Part 3: Clustering & Association Rules

  • Introduction to Clustering
  • Similarity and Distance Measures
  • Hierarchical Algorithm(Divisive Clustering)
  • Partitional Algorithm(Minimum Spanning Tree, Nearest Neighbour)
  • Clustering Large Database(CURE)
  • Introduction to association
  • Basic Algorithm(Apriori)
  • Parallel & Distributed(Data Parallelism)
  • Incremental Rules
  • Association Rule Techniques(Generalised Multiple Level)

Part 4: Advanced Mining

  • Web Mining
  • Web Content Mining
  • Introduction to Spatial Mining & its Primitives
  • Spatial Classification Algorithm(ID3 Extension)
  • Spatial Clustering Algorithm(SD)
  • Introduction to Temoral Mining
  • Time Series
  • Temporal Association Rule

Part 5: Data Mining Environment

  • Case Study in Building Business Environemnt
  • Application of Data Mining In Government
  • National Data Warehouse and Case Studies

If you encounter an error in this series or want to add or suggest something, please reach out to me via my email.