1. DataMining By Guan Hang Su CS157A section 2 fall 2005

2. Outline Overview ---- Define Data Mining ---- Foundation of Data Mining ---- Scope of Data Mining ---- Techniques in data mining ----Applications

3. What is DataMining? Discovering “hidden value” in your data warehouse

4. Define Data Mining The automated extraction of hidden predictive information from (large) databases Three key words:  Automated  Hidden  Predictive Implicit is a statistical methodology  Data mining lets you be proactive  Prospective rather than Retrospective

5. The Foundations of Data Mining Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.

6. The Foundations of Data Mining (continue) Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature: Massive data collection Powerful multiprocessor computers Data mining algorithms

7. The Scope of Data Mining Data mining derives its name from the similarities between searching for valuable business information in a large database Example — finding linked products in gigabytes of store scanner data and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides.

8. The Scope of Data Mining (cont..) Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities: Automated prediction of trends and behaviors Automated discovery of previously unknown patterns.

9. The Scope of Data Mining (cont..) Automated prediction of trends and behaviors --- Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data. Typical example of a predictive problem: 1)targeted marketing. 2) forecasting bankruptcy

10. The Scope of Data Mining (cont..) Automated discovery of previously unknown patterns ---- Data mining tools sweep through databases and identify previously hidden patterns in one step. Example of pattern discovery: The analysis of retail sales data to identify seemingly unrelated products that are often purchased together Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

11. Techniques in data mining The most commonly used techniques in data mining: Artificial neural networks Decision trees Genetic algorithms Nearest neighbor method Rule induction

12. Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure. Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID)

13. Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution. Nearest Neighbor. A data mining technique that performs prediction by finding the prediction value of records (near neighbors) similar to the record to be predicted.

14. Rule induction: The extraction of useful if-then rules from data based on statistical significance Other Techniques : Bayesian networks ----- Naïve Bayes Support vector machines Many more…..

15. Decision Trees Nearest Neighbor classification Neural Networks Rule Induction K-means Clustering

16. Example of Neural Network  Difficult interpretation  Tends to ‘overfit’ the data  Extensive amount of training time  A lot of data preparation  Works with all data types Output Hidden layer Input layer

17. Example of Rule of induction Description  Produces decision trees: income < $40K  job > 5 yrs then good risk  job < 5 yrs then bad risk income > $40K  high debt then bad risk  low debt then good risk  Or Rule Sets: Rule #1 for good risk:  if income > $40K  if low debt Rule #2 for good risk:  if income < $40K  if job > 5 years

18. K-Nearest-Neighbor (kNN) Models Use entire training database as the model Find nearest data point and do the same thing as you did for that record Very easy to implement. More difficult to use in production. Disadvantage: Huge Models 0 Doses 1000 100 Age

19. Example of Decision Trees

20. How Data Mining Works How exactly is data mining able to tell you important things that you didn't know or what is going to happen next? The technique that is used to perform these feats in data mining is called modeling. Modeling is simply the act of building a model in one situation where you know the answer and then applying it to another situation that you don't.

21. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the computer must run through that data and distill the characteristics of the data that should go into the model Once the model is built it can then be used in similar situations where you don't know the answer

22. Some results of Data Mining Forecasting what may happen in the future. Classifying people or things into groups by recognizing patterns. Clustering people or things into groups based on their attributes. Sequencing what events are likely to lead to later events

23. Example For example, say that you are the director of marketing for a telecommunications company and you'd like to acquire some new long distance phone customers. 1)randomly mail out the coupon to general population. 2) or use your business experience stored in your database to build a model, then choose the right target.

24. Cont.. As the marketing director you have access to a lot of information about all of your customers: their age, sex, credit history and long distance calling usage. The problem is that you don't know the long distance calling usage of these prospects (since they are most likely now customers of your competition).

25. We 'd like to concentrate on those prospects who have large amounts of long distance usage.We can accomplish this by building a model CustPros General information (e.g. demographic data) Known Proprietary information (e.g. customer transactions) KnownTarget

26. For instance, a simple model for a telecommunications company might be: 98% of my customers who make more than $60,000/year spend more than $80/month on long distance. With this model in hand new customers can be selectively targeted

27. Architecture for Data Mining To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining.

28. illustrates an architecture for advanced analysis in a large data warehouse

29. Data Mining Applications The US Drug Enforcement Agency needed to be more effective in their drug “busts”. Analyzed suspects’ cell phone usage to focus investigations.

30. HSBC need to cross-sell more effectively by identifying profiles that would be interested in higher yielding investments. Reduced direct mail costs by 30% while garnering 95% of the campaign’s revenue.

31. Bibliography http://www.thearling.com/dmintro/dmintro_frame.ht m http://www.thearling.com/dmintro/dmintro_frame.ht m http://www.thearling.com/text/dwhite/dmwhite.htm http://www.cs.sjsu.edu/faculty/lee/cs157/25SpL22D ataMining.ppt http://www.cs.sjsu.edu/faculty/lee/cs157/25SpL22D ataMining.ppt http://www.oracle.com/technology/products/bi/odm/i ndex.html