The document discusses data mining as a process of extracting useful information from large datasets, essential for transforming data into actionable insights. It outlines the stages of data mining, including data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Additionally, various applications of data mining are presented across industries such as finance, insurance, telecommunication, and scientific research.

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Presented By:
Sharif Hossain-1162
Presented To:
 ASHIQUR RAHMAN

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 Data mining: discovering interesting patterns from large
amounts of data.
What Is Data Mining?

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4.  We need data mining for
 Transforming data into useful information to users
 Present data in useful format
 Provide data access to business analyst, Information
technology professionals
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5.  An abundance of data
 Super Market Scanners, POS data
 Credit cards transactions
 Call Center records
 ATM Machines
 Demographic data
 Sensor Networks
 Cameras
 Web server logs
 Customer web site trails
 Geographic Information System
 National Medical Records
 Weather Images
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6.  Data Mining is the technique used to carry out KDD.
 Data Mining turns data into information and then to
knowledge
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Information
Data
Knowledge

7.  RapidMiner and Weka – Defining data mining process
 Top 8 data mining software in 2008
1. Angoss software
2. Infor CRM Epiphany
3. Portrait Software
4. SAS
5. SPSS
6. ThinkAnalytics
7. Unica
8. Viscovery
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8. 1. Data cleaning
 To remove noise and inconsistent data
2. Data integration
 To integrate (compile) multiple data sources
3. Data selection
 Data relevant to analysis is selected
4. Data transformation
 Summary normalization aggregation operations are
performed (convert data into two dimension form) and
consolidate the data
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9. 5. Data mining
Intelligent methods are applied to the data to discover
knowledge or patterns
6. Pattern evaluation
Evaluation of the interesting patterns by thresholding
7. Knowledge Discovery
Visualization and presentation methods are used to present
the mined knowledge to the user.
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10. Industry
Finance
Insurance
Telecommunication
Transport
Consumer goods
Scientific Research
Utilities
Application
 Credit Card Analysis
 Claims, Fraud Analysis
 Call record analysis
 Logistics management
 Promotion analysis
 Image, Video, Speech
 Power usage analysis

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 Financial Industry, Banks, Businesses, E-commerce
◦ Stock and investment analysis
◦ Identify loyal customers and risky customer
◦ Predict customer spending
 Database analysis and decision support
◦ Market analysis and management
 target marketing, customer relation management, market basket
analysis.
◦ Risk analysis and management
 Forecasting, quality control, competitive analysis
◦ Fraud detection and management
Others Applications
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