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

Presented

By

Kallepalli Vijay

Agenda

Introduction Motivation Background Solution Results Conclusion and Future work

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

Introduction (Con…)

If investors are given adequate information– regarding stock market trends

Investors can invest money accordingly – maximum profits.

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

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

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 PQ

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

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

Solution (Con…)

Solution (Con…)

Results

Results (Con…)

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

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

Thanks!