This document provides an overview of data mining techniques and concepts. It defines data mining as the process of discovering interesting patterns and knowledge from large amounts of data. The key steps involved are data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. The document also discusses data sources, major applications of data mining, and challenges.

1. Data Mining
Techniques
UNIT-I

2. The Introduction to Data mining
Systems
• What is Data?
• What is Database?
• What is Database Management System?

3. The Introduction to Data mining
Systems
• Why Data Mining?
• Data Collection and Data Availability
• Major sources of abundant data

4. Data Mining
• What is Data Mining?
• Data mining (knowledge discovery from data)
– Extraction of interesting (non-trivial, implicit, previously unknown and
potentially useful) patterns or knowledge from huge amount of data
• Alternative names
– Knowledge discovery (mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology, data dredging,
information harvesting, business intelligence, etc.
• Is everything “data mining”?
– Simple search and query processing
– (Deductive) expert systems

5. Examples
• Examples of Data Mining
1. Marketing
2. Banking
3. Government
4. Health Care
5. Education
6. Retail Industry
7. Logistics and supply chain

6. Steps involved in Data Mining Process

7. Large-scale Data is Everywhere!
 There has been enormous data growth in both commercial and scientific
databases due to advances in data generation and collection technologies
Cyber Security E-Commerce
Traffic
Patterns
Social Networking: Twitter
Sensor Networks
Computational
Simulations

8. Why Data Mining? Commercial
Viewpoint
• Lots of data is being collected
and warehoused
– Web data
• Yahoo has Peta Bytes of web data
• Facebook has billions of active users
– purchases at department/
grocery stores, e-commerce
• Amazon handles millions of visits/day
– Bank/Credit Card transactions
• Computers have become cheaper and more powerful
• Competitive Pressure is Strong
– Provide better, customized services for an edge (e.g. in Customer
Relationship Management)

9. Great Opportunities to Solve Society’s Major Problems
Improving health care and reducing costs
Finding alternative/ green energy sources
Predicting the impact of climate change
Reducing hunger and poverty by
increasing agriculture production

10. Evolution of Data Mining

11. Evolution of Database Technology-
Summary
• 1960s-Data Collection, DB creation, network
DBMS
• 1970-Relational Data Model, relational DBMS
• 1980-RDBMS,Advanced data models,
Application oriented DBMS
• 2000s-stream data management and mining,
DM & its applications.

12. Basic Concepts
• Classification
• Clustering
• Supervised Learning
• Unsupervised Learning

13. Knowledge Discovery Process

14. Steps in the process of Knowledge
Discovery(KDD Process)
• Data Cleaning
• Data Integration
• Data Selection
• Data Transformation
• Data Mining
• Pattern Evaluation
• Knowledge Presentation

15. Kinds of Data
• What kinds of Data can be mined?
• Database Data
• Data Warehouses
• Transactional Data
• Other Kinds of Data

16. Database Data
• Database Management System(DBMS)
• Relational Data base: tables, attributes, tuples
• Entity-Relationship(ER Model)
• Database queries
• Mining relational database

18. Data Warehouses
• What is data warehouse?
• What is data cube?
• OLAP (Online Analytical Processing)
operations: drill down, roll up

19. Typical framework of a data
warehouse for All Electronics

20. A Multidimensional data cube ,commonly used for data warehousing.(a)
showing summarized data for All Electronics and b)showing summarized
data resulting from drill-down and roll-up operations on the cube .

21. Transactional Data

22. Other Kinds of Data
• Time related or sequence data
• Data streams
• Spatial data
• Engineering design data
• Hypertext and Multimedia data
• Graph and Networked data

23. Kinds of Patterns(Data Mining
Functionalities)
• Data Mining Tasks: Descriptive and Predictive
• DM functionalities includes:
• Characterization and Discrimination
• Mining frequent patterns, Associations and
Correlations
• Classification and Regression
• Clustering Analysis
• Outlier Analysis
• Are all patterns are interesting

24. Class/Concept Description: Characterization
and Discrimination
• Eg., In all electronics store, class of items for sale include computers and
printers and concepts of customers include big Spenders and budget
Spenders
• Data Characterization
• Methods for data summarization and characterization:simple data
summaries based on statistics measures and plots,data cube based OLAP
operations,attribute oriented induction techniques.
• Output of Data Characterization and Example for Data Characterization
• Data Discrimination
• Output of Data Discrimination and Example for Data Discrimination

25. Mining Frequent patterns,
association and correlations
• Frequent patterns:Frequent itemset,frequent subsequences,frequent
substructure
• Association Analysis:
• Eg:association rule- buys(x,”computer”) => buys(x,”software”)
predicate
[support=1%,confidence=50%]
confidence(certainity),support(under analysis)
• Single dimensional association rule
• Multidimensional association rule
Age(x,”20..29”)^ income(x,”40..49K”) =>buys(x,”laptops”)
[support=2%,confidence=60%]
• Association should satisfy both minimum threshold and minimum
confidence

26. Classification and regression for
predictive analysis
• What is classification and its example?
• Training data and test data
• Derived models presented by
1. Classification rules(If-then-rules)
2. Decision tree
3. Mathematical formulae
4. Neural networks
• Regression analysis

28. Cluster analysis and outlier analysis

29. Are all pattern interesting?
• Support(x=>y) =p(x U y)
• Confidence(x =>y) =p(y/x)
• Accuracy
• Coverage
• Unexpected Vs expected

30. Data mining Technologies

31. Statistics
• It is a collection, analysis, interpretation or
explanation and presentation of data.
• Statistical model
• Statistical description
• Inferential statistics or predictive statistics
• Statistical hypothesis test

32. Machine Learning
• What is machine learning?
• Classic problems in machine learning are:
• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Active learning

35. Database System, Data warehouses
& Information retrieval
• Database systems research
• Data warehouse
• Information retrieval
• Language model
• Topic model

36. Data Mining Applications
• Business Intelligence
• Web Search Engines

37. Issues in Data Mining
• Mining Methodology
• User Interaction
• Efficiency and Scalability
• Diversity of database types
• Data Mining and society

38. Mining Methodology
• Mining various and new kinds of knowledge
• Mining knowledge in multidimensional space
• Data Mining-an interdisciplinary effort
• Boosting the power of discovery in a networked
environment
• Handling uncertainty, noise or incompleteness of data
• Pattern evaluation and pattern-or constraint-guided
mining

39. User Interaction
• Interactive mining
• Incorporation of background knowledge
• Ad hoc data mining and data mining query
languages
• Presentation and visualization of data mining
results

40. Efficiency and Scalability
• Efficiency and scalability of data mining
algorithms
• Parallel, distributed and incremental mining
algorithms
• Cloud computing and cluster computing

41. Diversity of database types
• Handling complex types of data
• Mining dynamic, networked and global data
repositories

42. Data Mining and Society
• Social impacts of data mining
• Privacy-preserving data mining
• Invisible data mining

43. Summary
• Data mining: Discovering interesting patterns and knowledge from
massive amount of data
• A natural evolution of database technology, in great demand, with
wide applications
• A KDD process includes data cleaning, data integration, data
selection, transformation, data mining, pattern evaluation, and
knowledge presentation
• Mining can be performed in a variety of data
• Data mining functionalities: characterization, discrimination,
association, classification, clustering, outlier and trend analysis, etc.
• Data mining technologies and applications
• Major issues in data mining

44. DATAWAREHOUSE:BASIC
CONCEPTS
• What is data warehouse?
• Subject-oriented, integrated, time- variant,
nonvolatile
• How are organizations using the information from
data warehouses?
- Knowledge workers
• Query driven approach(Traditional Database
approach)
• Update driven approach(Data warehousing approach)

45. Difference between operational database
systems and data warehouse
• What is OLTP and OLAP?
- Online transaction processing(OLTP)
- Online analytical processing (OLAP)
• Major features /differences between OLTP & OLAP
systems
-User and system orientation
-Data Contents
-Database design
-View
-Access patterns

46. Why have a separate Data
Warehouse?
• DBMS
• Data Warehouse
• Different functions and different data
-Missing data
-Data consolidation
-Data Quality

47. Data warehousing: A multiered
architecture
• Bottom tier: Data Warehouse Server
-Data Sources
-Gateways
• Middle tier: OLAP server
-ROLAP(Relational OLAP)server
-MOLAP(Multidimensional OLAP)
• Top tier: Front-end tools

48. A three-tier data warehousing
architecture

49. Data Warehouse Models
• Enterprise warehouse
• Data Mart
• Virtual warehouse
• Types of Data Mart
-Independent Data Mart
-Dependent Data Mart
• Data warehouse development
-Top-down approach &Bottom-up approach to
DataWarehouse development

50. A recommended approach for
data warehouse development

51. Data Warehouse Models
• High-level corporate data model is defined
within short period
• Enterprise and Department Data Marts
• Distributed Data Marts
• Multitier Data Warehouse

52. Extraction, Transformation and
Loading
• Data Extraction
• Data Cleaning
• Data Transformation
• Load
• Refresh

53. Metadata Repository
• Description of the data warehouse structure
• Operational metadata
-Data lineage
-Currency of data
-Monitoring Information
• Algorithms used for summarization
• Mapping from the operational environment to data
warehouse
• Data related to system performance
• Business metadata

54. Data warehouse modeling: Data
Cube and OLAP
• What is data cube?
• Facts
• Fact table
• Lattice of cuboids
• Base cuboid
• Apex cuboid

55. 2D,3D,4D-Data Cube

56. 3D Data Cube

57. 4DData Cube

59. Schemas for multidimensional
data models
• Star schema
• Snow flake schema
• Fact constellation schema

60. Star Schema

61. Snow flake Schema

62. Fact Constellation

63. Schema Hierarchy Vs Set-Grouping
Hierarchy
• Data warehouse Vs Data Mart
• Dimensions: The role of Concept Hierarchies
-set of low level concepts to higher level,
more general concepts

64. Set grouping Hierarchy
• Discretizing or grouping values for a given
dimension or attributes

65. Measures: Their Categorization and
Computation
• Distributive
• Algebraic
• Holistic

66. Typical OLAP operations
• Roll-up(drill-up)
• Drill –down(reverse of roll-up)
• Slice & Dice
• Pivot
• Other operations: drill-across, drill-through
• OLAP systems Vs Statistical Databases
-Starnet query model for querying
multidimensional database:radial lines,foot
print

67. Slice,dice,pivot

68. starnet query model

69. END OF THE UNIT-1