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.
These notes are specially designed in pdf format for easy download and contain Power point presentations Lecture notes in simple and easy languages with full diagrams of architecture and its full explanation.
The Following Lines provides the topics in the specific notes with their download links:-
UNIT‐1: Introduction of Data Mining , Data warehouse and OLAP
- Motivation: Why data mining?
- What is data mining?
- Data Mining: On what kind of data?
- Data mining functionality?
- Classification of data mining systems
- Major issues in data mining
- What is a data warehouse?
- A multi‐dimensional data model
- Data warehouse architecture
- Data warehouse implementation
- Data warehouse implementation
- From data warehousing to data mining
UNIT‐2: Data Pre-processing
- Why preprocess the data?
- Data cleaning
- Data integration and transformation
- Data reduction
- Discretization and concept hierarch generation
UNIT‐3: Data Mining Primitives, Languages, and System Architectures
- Data mining primitives: What defines a data mining task?
- A data mining query language
- Design graphical user interfaces based on a data mining query language
- Architecture of data mining systems
UNIT‐4: Characterization and Comparison
- What is concept description?
- Data generalization and summarization‐based characterization
- Analytical characterization:Analysis of attribute relevance
- Mining class comparisons: Discriminating between different classes
- Mining descriptive statistical measures in large databases
UNIT‐5: Mining Association Rules in Large Databases
- Association rule mining
- Mining single‐dimensional Boolean association rules from transactional databases
- Mining multilevel association rules from transactional databases
- Mining multidimensional association rules from transactional databases and data warehouse
- From association mining to correlation analysis
- Constraint‐based association mining
UNIT‐6: Classification and Prediction
- What is classification? What is prediction?
- Issues regarding classification and prediction
- Classification by decision tree induction
- Bayesian Classification
- Classification by backpropagation
- Classification based on concepts from association rule mining
- Other Classification Methods
- Classification accuracy
UNIT‐7: Cluster Analysis
- What is Cluster Analysis?
- Types of Data in Cluster Analysis
- A Categorization of Major Clustering Methods
- Partitioning Methods
- Hierarchical Methods
- Density‐Based Methods
- Grid‐Based Methods
- Model‐Based Clustering Methods
- Outlier Analysis
UNIT‐8: Mining Complex Types of Data
- Multidimensional analysis and descriptive mining of complex data objects
- Mining spatial databases
- Mining multimedia databases
- Mining time‐series and sequence data
- Mining text databases
- Mining the World‐Wide Web
Download Complete Package:-
Feel free to comment below regarding notes.
If you wants some more notes on any of the topics please mail to us or comment below. We will provide you as soon as possible and if you want your’s notes to be published on our site then feel free to contribute on EduTechLearners or mail your content to email@example.com ( The contents will be published by your Name).