The goal of data mining is to unearth relationships in data that may provide useful insights. Data mining tools can sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together.

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Data Warehousing & Data Mining Study Materials & Notes - DWDM Text Book pdf

Data Warehousing & Data Mining (DWDM) Materials & Notes. DWDM Unit Wise Lecture Notes and Study Materials in pdf format for Engineering Students. This DWDM Study Material and DWDM Notes & Book has covered every single topic which is essential for B.Tech/ BE Students. Data Warehousing & Data Mining Study Materials provided here is specifically prepared for JNTUH JNTUK JNTUA R13, R10, R09 Students but all other University students can also download it as it has covered every single important chapter. So all students seeking DWDM Engineering Book for JNTU Hyderabad, JNTU Kakinada, JNTU Anantapur, Guru Gobind Singh Indraprastha University, Anna University, PTU, UPTU, JMI, University of Mumbai, Osmania University, GGU, WBUT, LPU, SMU, Galgotia's and other universities can download Data Warehousing & Data Mining (DWDM) Materials & Notes, Lecture Notes, Books and Class Notes from this page.

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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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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

13. Why preprocess the data?
14. Data cleaning
15. Data integration and transformation
16. Data reduction
17. Discretization and concept hierarch generation

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

18. Data mining primitives: What defines a data mining task?
19. A data mining query language
20. Design graphical user interfaces based on a data mining query language
21. Architecture of data mining systems

UNIT‐4: Characterization and Comparison

22. What is concept description?
23. Data generalization and summarization‐based characterization
24. Analytical characterization:Analysis of attribute relevance
25. Mining class comparisons: Discriminating between different classes
26. Mining descriptive statistical measures in large databases

UNIT‐5: Mining Association Rules in Large Databases

27. Association rule mining
28. Mining single‐dimensional Boolean association rules from transactional databases
29. Mining multilevel association rules from transactional databases
30. Mining multidimensional association rules from transactional databases and data warehouse
31. From association mining to correlation analysis
32. Constraint‐based association mining

UNIT‐6: Classification and Prediction

32. What is classification? What is prediction?
33. Issues regarding classification and prediction
34. Classification by decision tree induction
35. Bayesian Classification
36. Classification by backpropagation
37. Classification based on concepts from association rule mining
38. Other Classification Methods
39. Prediction
40. Classification accuracy

UNIT‐7: Cluster Analysis

41. What is Cluster Analysis?
42. Types of Data in Cluster Analysis
43. A Categorization of Major Clustering Methods
44. Partitioning Methods
45. Hierarchical Methods
46. Density‐Based Methods
47. Grid‐Based Methods
48. Model‐Based Clustering Methods
49. Outlier Analysis

UNIT‐8: Mining Complex Types of Data

50. Multidimensional analysis and descriptive mining of complex data objects
51. Mining spatial databases
52. Mining multimedia databases
53. Mining time‐series and sequence data
54. Mining text databases
55. Mining the World‐Wide Web

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