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Data Mining: An Introduction Wing Kee Ho Xiaohua Luan.

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Presentation on theme: "Data Mining: An Introduction Wing Kee Ho Xiaohua Luan."— Presentation transcript:

1 Data Mining: An Introduction Wing Kee Ho Xiaohua Luan

2 Outline of Today Presentation Definition of data mining Comparison of Data mining vs. DBMS Sample data mining tasks in daily life Data mining development

3 Definition of Data Mining The nontrivial extraction of implicit, previously unknown, and potentially useful information

4 Why do we need to mine the data? Too much data and too little information There is a need to extract useful information from the data and to interpret the data

5 Comparison of DBMS and Data Mining DBMS Query – SQL Output – Precise – Subset of database Data Mining Query –Not precise query lang Output – Fuzzy – Not in Subset of database

6 Data Mining or DBMS? Last months sales for each product The profit forecast on next month List of customers who lapsed their policy The characteristics of customers who lapsed their policies

7 Sample Data Mining Example Association Rules Clustering Time-Series Forecasting

8 Association Rule --- using Harris Teeter as an example TransactionItem t1milk, chip, bread, salsa, coke t2banana, chip, rice, salsa t3salsa, coke, banana, chip t4milk, lettuce, coke, rice, salsa, bread t5lettuce, salsa, bread, coke, chip, milk

9 Association Rule, cont Objective: Identify items that occur together Support of {salsa, chip} is 80%, Support of {bread, milk} is 60% Data is useful for shelving, merchandizing, and pricing.

10 Clustering -- market segmentation as an example Each point represent the characters of a customer

11 Clustering, cont Objective: grouping members that have similar characteristics together Widely applied on fraud detection, business and finance, science

12 Statistical Analysis Regression: Time Series: Housing Price Area (sq.feet) Sales Volume Time

13 More data mining techniques Decision Tree Neural Network Combination of several data mining techniques

14 Implications for different interest parties: Database users: --- new skills to explore to secure your job! Database developers: --- develop new functions, and better interface General Public: --- less privacy, or more convenient?

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