Data Warehousing And Data Mining Lecture Notes Pdf

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Data Warehousing and Data Mining Notes PDF can be easily download from EduTechLearners without signup or login. Data Warehousing and Data Mining is a subject for students of B.tech of Computer Science & Engineering (CSE).These notes provides information about the Data Warehousing and Data Mining In full details. These notes can be downloaded Unit Wise as well as Zip file files containing all the Unit.

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Introduction to Data Warehousing and Business Intelligence Prof. Dipak Ramoliya 2170715 – Data Mining & Business Intelligence 7 3. Hybrid Data Marts A hybrid data mart allows you to combine input from sources other than a data warehouse. This could be useful for many situations, especially when you need ad hoc integration, such as after. Data warehouse Architecture and its seven components 1. Data sourcing, cleanup, transformation, and migration tools 2. Metadata repository 3. Warehouse/database technology 4. Data marts 5. Data query, reporting, analysis, and mining tools 6. Data warehouse administration and management 7. Information delivery system.

Alif novel episode 11. The Following Lines provides the topics in the specific notes with their download links:-

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

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

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

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

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

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

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

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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