Data Mining D t Mi i
Module 1
Introduction to Data Mining

Dr. Jason T.L. Wang, Professor
Department of Computer Science New Jersey Institute of Technology
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Data Management: Its Evolution
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1960s:
– File management and network DBMS

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1970s:
– Relational DBMS

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1980s: 980s
– Non-first normal form, extended-relational, OO,

deductive databases and application-oriented DBMS pp (spatial, scientific, CAD/CAM, etc.)
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1990s - present: p
– Data mining, digital library, and Web databases – Cloud databases, data science, and Big Data
Data Mining © Jason Wang 2

Data Mining: Its Definition
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Data mining (knowledge discovery in databases): )
– Extraction of interesting (non-trivial, implicit,

previously unknown and potentially useful) information or patterns from data in large databases
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Alternative names:
– Knowledge discovery (mining) in databases

(KDD), knowledge extraction, data/pattern analysis, analysis data archeology, data dredging archeology dredging, information harvesting, etc.
Data Mining © Jason Wang 3

Data Mining: A Multidisciplinary Field
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Pattern Recognition  Machine Learning  Databases  St ti ti Statistics  Information Visualization

Data Mining

© Jason Wang

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Data to be mined
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Text databases  Web databases  Scientific and biological databases  Transactional databases

Data Mining

© Jason Wang

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Knowledge to be discovered K l d t b di d
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Association (correlation)
– Multi-dimensional vs. single-dimensional association – age(X “20 29”) ^ income(X “20 29K”)  buys(X “PC”) age(X, 20..29 ) income(X, 20..29K ) buys(X, PC )

[support = 2%, confidence = 60%]
– contains(X, “computer”)  contains(X, “software”) [1%, computer ) software )

75%]

Data Mining

© Jason Wang

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Knowledge to be discovered g (cont.)
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Classification
– Finding models