Data Mining Part 1

Kim Graff
Flashcards by Kim Graff, updated more than 1 year ago
Kim Graff
Created by Kim Graff about 6 years ago
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Data Mining course final

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Data Mining is.. Data mining—core of knowledge discovery process
Data Mining Process
Typical Data Mining System
What is OLTP? What does it do? On-line Transaction Processing Operational DBMS Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc
What is OLAP and what does it do? On-Line Analytical Processing Data Warehouse Major task of data warehouse system Data analysis and decision making
Distinct Features of OLTP vs. OLAP User and System Orientation Customer vs. Market
Distinct Features of OLTP vs. OLAP Data Contents Current, detailed vs. historical, consolidated
Distinct Features of OLTP vs. OLAP Database design ER + application vs. star + subject
Distinct Features of OLTP vs. OLAP View Current, local vs. evolutionary, integrated
Distinct Features of OLTP vs. OLAP Access Patterns Updated vs. read-only but complex queries
OLTP breakdown USER: clerk, IT professional FUNCTION: day to day operations DB DESIGN: application-oriented DATA: current, up-to-date, detailed, flat relational isolated USAGE: reptitive ACCESS: read/write index/hash on prim key UNIT OF WORK: short, simple transaction # RECORDS ACCESS: tens # USERS: thousands DB SIZE: 100MB-GB METRIC: transaction throughput
OLAP breakdown USERS: knowledge worker FUNCTION: decision support DB DESIGN: subject-oriented DATA: historical, summarized, multidemensional, integrated, consolidated USAGE: ad-hoc ACCESS; lots of scans UNIT OF WORK: Complex queries # RECORDS ACCESSED: millions # USERS: hundreds DB SIZE: 10GB-TB METRIC: query throughput, réponse
CUBE: A Lattice of Cuboids
Example of Fact Constellation
Generating Association Rules for Frequent Itemsets Once the frequent itemsets have been found, generation strong association rules from them is straight forward An association rule A -> B is STRONG if it satisfies both min support and min confidence
Generating Association Rules from Frequent Itemsets METHODS 1. For each frequent items I, generation all non-empty subsets of I 2. For every non-empty subset s of I, output rules s-> (i-s) if con(s->(i-s)) >/= min_conf
LIFT is Measuring of dependent/correlated events
Process 1: Model Construction
Process (2) Using the Model in Prediction
Attribute Selection Measure: Information Gain (ID3)
Attribute Selection: Info Gain
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