Chapter 11 4 Topic data mining

7/31/2019 Chapter 11 4 Topic data mining

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Social impacts of data mining


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Is Data Mining a Hype or Will It Be Persistent?

Data mining is a technology

Technological life cycle

Innovators

Early adopters

Chasm

Early majority

Late majority Laggards


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Life Cycle of Technology Adoption

Data mining is at Chasm!? Existing data mining systems are too generic Need business-specific data mining solutions and smooth

integration of business logic with data mining functions


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Data Mining: Managers' Business or Everyone's?

Data mining will surely be an important tool formanagers decision making Bill Gates: Business @ the speed of thought

The amount of the available data is increasing, and

data mining systems will be more affordable Multiple personal uses

Mine your family's medical history to identify genetically-related medical conditions

Mine the records of the companies you deal with

Mine data on stocks and company performance, etc.

Invisible data mining

Build data mining functions into many intelligent tools


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Social Impacts: Threat to Privacy and Data

Security?

Is data mining a threat to privacy and data security? Big Brother, Big Banker, and Big Business are carefully

watching you

Profiling information is collected every time

credit card, debit card, supermarket loyalty card, or frequent flyercard, or apply for any of the above

You surf the Web, rent a video, fill out a contest entry form,

You pay for prescription drugs, or present you medical care numberwhen visiting the doctor

Collection of personal data may be beneficial for companies

and consumers, there is also potential for misuse Medical Records, Employee Evaluations, etc.


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Protect Privacy and Data Security

Fair information practices International guidelines for data privacy protection

Cover aspects relating to data collection, purpose, use,quality, openness, individual participation, andaccountability

Purpose specification and use limitation

Openness: Individuals have the right to know whatinformation is collected about them, who has access to thedata, and how the data are being used

Develop and use data security-enhancing techniques Blind signatures

Biometric encryption

Anonymous databases


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11.5 Trends in Data Mining

Application exploration development of application-specific data mining system

Invisible data mining (mining as built-in function)

Scalable data mining methods

Constraint-based mining: use of constraints to guide datamining systems in their search for interesting patterns

Integration of data mining with database systems,

data warehouse systems, and Web database

systems This will ensure data availability, data mining portability,

scalability, high performance, and an integrated

information processing environment for multidimensional

data analysis and exploration.


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Standardization of data mining language A standard will facilitate systematic development, improve

interoperability, and promote the education and use of

data mining systems in industry and society

Visual data mining The systematic study and development of visual data

mining techniques will facilitate the promotion and use of

data mining as a tool for data analysis.

New methods for mining complex types of data More research is required towards the integration of data

mining methods with existing data analysis techniques for

the complex types of data


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Web mining Web content mining, Weblog mining, and data mining services

on the Internet will become one of the most important andflourishing subfields in data mining.

Distributed data mining:

Traditional data mining methods, designed to work at acentralized location, do not work well in many of the distributedcomputing environments present today (e.g., the Internet,intranets, local area networks, high-speed wireless networks,and sensor networks).

Real-time or time-critical data mining: Many applications involving stream data (such as e-commerce,

Web mining, stock analysis, intrusion detection, mobile datamining, and data mining for counterterrorism) require dynamicdata mining models to be built in real time. Additionaldevelopment is needed in this area.


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Privacy protection and information security in data mining An abundance of recorded personal information available in

electronic forms and on the Web, coupled with increasingly

powerful data mining tools, poses a threat to our privacy and

data security. Growing interest in data mining for

counterterrorism also adds to the threat. Graph mining, link analysis, and social network analysis:

Graph mining, link analysis, and social network analysis are

useful for capturing sequential, topological, geo- metric, and

other relational characteristics of many scientific data sets and

social data sets

Multirelational and multidatabase data mining:

Most data mining approaches search for patterns in a single

relational table or in a single database.

Chapter 11 4 Topic data mining

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