Application of Apriori Algorithm to Derive Association Rules Over Finance Data Set

1. Application of Apriori Algorithm to Derive Association Rules Over Finance Data Set Presented By Kallepalli Vijay Instructor: Dr. Ruppa Thulasiram

2. Agenda • Introduction • Motivation • Background • Solution • Results • Conclusion and Future work

3. Introduction • Stock exchanges maintain a log of events • rise in stock price, option value, number of stocks sold • Investors predict the market trends based on available log information • Stock market is highly unpredictable • The values in the log change very drastically with time

4. Introduction (Con…) • If investors are given adequate information • regarding stock market trends • Investors can invest money accordingly • maximum profits.

5. Motivation • Data in the log is very huge • Data contains hidden details • Manually identifying the hidden details • cumbersome process • Apply Apriori to retrieve hidden details • Prior deriving large item data has to be classified

6. Background • Data mining: A process of extracting unknown patterns, facts and relations from large database • Data mining means knowledge discovery from large databases • Association rules in data mining involves in detecting which items tend to occur together in transactions

7. Background (Con…) • Association rules in data mining was first proposed by Agrawal, Imielinski and Swami in 1993 • Ex: Customer who purchase one item are likely to purchase another item. • Consider A transaction is a set of items • T = {i1, i2,……it} • T  I, where I is the set of all possible items {i1, i2,……in} • P Q, Where P  I, Q  I, and PQ

8. Solution • Finance data is quantitative • Classify data into regular intervals • Map the classified data to an index value • Index values range from 0 through 143 • 0 through 35 represent stocks opening value • 36 through 71 represent stocks day high value • 72 through 107 represent stocks day low value • 108 through 143 represent stocks closing value

9. Solution (Con…) • Finance data of the Apple Computers Inc is read from a text file • Eg1: Stock opening value ranging between 10.0 and 10.5 is mapped to an index value 0 • Eg2: Stock high value ranging between 10.0 and 10.5 is mapped to an index value 36 • Apply Apriori algorithm on the mapped indices to derive association rules

10. Solution (Con…)

11. Solution (Con…)

12. Results

13. Results (Con…)

14. Conclusions & Future work • Manually identifying hidden details is a tedious process • Classified the collected data into regular intervals • Applied apriori algorithm to derive large item sets • Derived large item sets and projected to the user in user readable form

15. Conclusions & Future work (Con…) • Classification of data plays important role • Correctness of association rules depends on the classification of the data • Selecting the length of the interval for classification is difficult • Fuzzy logic can applied on the data for classification

16. Thanks!