Data Warehouse and Data Mining Notes

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UNIT‐1:  Introduction of Data Mining , Data warehouse and OLAP

  1. Motivation: Why data mining?
  2. What is data mining?
  3.  Data Mining: On what kind of data?
  4. Data mining functionality?
  5. Classification of data mining systems
  6. Major issues in data mining
  7. What is a data warehouse?
  8. A multi‐dimensional data model
  9. Data warehouse architecture
  10. Data warehouse implementation
  11. Data warehouse implementation
  12. From data warehousing to data mining

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UNIT‐2: Data Pre-processing

  1. Why preprocess the data?
  2. Data cleaning
  3. Data integration and transformation
  4. Data reduction
  5. Discretization and concept hierarch generation

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UNIT‐3: Data Mining Primitives, Languages, and System Architectures

  1. Data mining primitives: What defines a data mining task?
  2. A data mining query language
  3. Design graphical user interfaces based on a data mining query language
  4. Architecture of data mining systems

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UNIT‐4: Characterization and Comparison

  1. What is concept description?
  2. Data generalization and summarization‐based characterization
  3. Analytical characterization:Analysis of attribute relevance
  4. Mining class comparisons: Discriminating between different classes
  5. Mining descriptive statistical measures in large databases

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UNIT‐5: Mining Association Rules in Large Databases

  1. Association rule mining
  2. Mining single‐dimensional Boolean association rules from transactional databases
  3. Mining multilevel association rules from transactional databases
  4. Mining multidimensional association rules from transactional databases and data warehouse
  5. From association mining to correlation analysis
  6. Constraint‐based association mining

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UNIT‐6: Classification and Prediction

  1. What is classification? What is prediction?
  2. Issues regarding classification and prediction
  3. Classification by decision tree induction
  4. Bayesian Classification
  5. Classification by backpropagation
  6. Classification based on concepts from association rule mining
  7. Other Classification Methods
  8. Prediction
  9. Classification accuracy

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UNIT‐7: Cluster Analysis

  1. What is Cluster Analysis?
  2. Types of Data in Cluster Analysis
  3.  A Categorization of Major Clustering Methods
  4. Partitioning Methods
  5. Hierarchical Methods
  6. Density‐Based Methods
  7. Grid‐Based Methods
  8. Model‐Based Clustering Methods
  9. Outlier Analysis

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UNIT‐8: Mining Complex Types of Data

  1. Multidimensional analysis and descriptive mining of complex data objects
  2. Mining spatial databases
  3. Mining multimedia databases
  4. Mining time‐series and sequence data
  5. Mining text databases
  6. Mining the World‐Wide Web

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One thought on “Data Warehouse and Data Mining Notes

  1. Hey admin, i don’t think that these notes are sufficient for exams . So can you please provide better notes having all topics in detail. Thank you

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