The document discusses the social impacts and trends of data mining, noting its rapid development due to societal computerization and the variety of data and approaches that present research challenges. It evaluates whether data mining is a transient hype or a sustainable business, highlighting its applications in customer relationship management and privacy concerns. Key trends include advancements in scalable methods, integration with database systems, and the need for privacy protection.

1. SOCIAL IMPACTS & TRENDS OF DATA
MINING
SUSHIL DHAKAL
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2. Data Privacy
Web MiningInformation Security
Data Mining
language
Visual Data Mining
Clickstreams
Biometric
Encryption
Blind
Signatures
Constraint-Based Mining
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OVERVIEW

3. INTRODUCTION
• The social impacts of data mining so fast due to
computerization of society.
• The diversity of data, data mining tasks, and
data mining approaches poses many
challenging research issues in data mining.
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4. IS DATA MINING A HYPE OR A
PERSISTENT, STEADILY GROWING
BUSINESS?
• Data mining is a technology.
1. Innovators
2. Early Adopters:
3. Chasm:
4. Early Majority:
5. Late Majority:
6. Laggards:
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5. IS DATA MINING A HYPE OR A
PERSISTENT, STEADILY GROWING
BUSINESS?
• Customer Relationship Management (CRM)
• Clickstreams
• Customer profiling
• Cross Market Analysis
• Resource Planning
• Competition
• Target marketing
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6. IS DATA MINING MERELY MANAGER’S
BUSINESS OR EVERYONE’S BUSINESS?
• Data on the Web and disks — benefit work and
daily life.
• Mine medical history —lifestyles and health.
• Mine the records of the companies —evaluate
their customer service.
• Mine data on stocks and company —financial
investments.
• Invisible data mining:
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7. IS DATA MINING A THREAT TO
PRIVACY AND DATA SECURITY?
• Profiling information can be collected every
time!
• Blind Signatures and Biometric Encryption
• Fair Information Practices:
1. Purpose specification and use limitation:
2. Openness:
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8. TRENDS OF DATA MINING
1. Application Exploration:
2. Scalable Data Mining Methods:
3. Integration of Data Mining with Database Systems, Data
Warehouse Systems, and Web Database Systems:
4. Standardization of Data Mining language:
5. Visual Data Mining:
6. New methods for mining complex types of data:
7. Web Mining:
8. Privacy protection and information security data mining:
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9. Any Queries?
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10. Thank You!
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