1. 3 Cases on Business Intelligence MIS

2. Case 1 : Market Basket Analysis with Association Rule
Data Collection
The data set for the tests is collected from a convenience store G
About 1,000 transactions are selected for a specific item
The data has information on Date_Purchased, Time_Purchased, POS, Employee Number, Receipt Number, Item_Name, Quantity, Purchase_Cost, and Description

3. Rice rolled in dried laver
The items in the data set are classified into 21 categories based on their characteristics as below:
Category
Definition 1
Processed food 12
Household items 2
Health beverage 13
Distilled liquor 3
Sweets 14
Newspaper 4
Rice rolled in dried laver 15
Yogurt 5
Frozen food 16
Milk 6
Tobacco 17
Juice 7
Instant noodle 18
Chocolate 8
Beer 19
Candy 9
Ice cream 20
Coffee 10
Bread 21
Carbonated beverage 11
Spring water

4. Association rule Model
A typical association rule has an implication of the form
A → B where A is an item set and B is an item set that contains only a single atomic condition
The support of an association rule is the percentage of records containing item sets A and B together
Support represents the usefulness of discovered rules
The confidence of a rule is the percentage of records containing item set A that also contain item set B
the confidence represents certainty of the detected association rule
Association rule technique is implemented with SAS E-Miner 4.0 with minimum support of 0.5%, and minimum confidence of 10%

5. Health beverage and Bread → Yogurt
Support (%)
Confidence (%)
Explanations
5.5 82
Chocolate → Milk
3.5 86
Processed food → Milk 57
Tobacco → Candy 2 50
Health beverage and Bread → Yogurt
1.5 67
Sweets and Bread → Beer
Newspaper → Tobacco 1
100
Milk and Candy
→ Chocolate
Ice cream →
Household items
Sweets and Frozen food
→ Instant noodle
Health beverage and
Frozen food → Coffee
Ice cream → Sweets
Association rules

6. Case 2 : Predicting Customers’ Decision on Purchasing in Web Stores
This case predicts customer behavior (buy or not buy) in web store
On-line customers are likely to move sequentially from item to item in web stores.

7. Given a session in a website, we examine our model to express detail of clickstream dataset in the session
A session means the entire processes from the initial visit to a site to the exit from the site
In Figure 2, the number of parentheses is the duration time(in seconds), which indicates how long the user stayed in the web page

8. An example of a customer session

9. Results and Discussion
We acquired a clickstream dataset and divided it into two, one is a dataset to buy, the other not to buy.
① AvgVisitTime: Total time of a session/No. of items visited
② SwitchCat: Total number of switching categories/No. of items visited
③ Re-visit: Total number of specific pages which are revisited
3 methods to predict customer behavior

10. Prediction accuracy of the models
To investigate the effectiveness of these 3 methods, we conducted experiments with these 3 methods respectively using test data. Table 1 shows the results.
Methods
Average of
Prediction (%)
AvgVisitTime
Train: 69.9
Test: 70.2
SwitchCat
Train: 57.9
Test: 60.8
Re-visit
Train: 60.0
Test: 60.0

11. A suggested method with association rule for predicting customer behavior

12. Business Intelligence Case 3 : Twitter mood predicts the stock market
Twitter Can Predict the Stock Market movement.
Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making.
Does this also apply to societies experience mood states that affect their collective decision making?
Is the public mood correlated or even predictive of economic indicators?
We investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time

13. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy).
Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others
We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA

14. Approach to predict stock index (Up or down)
Using OpinionFinder and Google-Profile of Mood States, they predict mood for stock market investment.
OpinionFinder is an open source tool measuring the opinions in twitter, which are positive or negative.
It is combined with the psychological tool, Profile of Mood State, which combines 72 emotional status words with GPOMS, producing 6 mood words: Calm, Alert, Sure, Vital, Kind, and Happy

15. Mood graph with 6 mood index

16. 12 combinations, positive/negative x 6 mood, are used to analyze 2700,000 twitter’s 9800,000 twitts between Feb to Dec
 The data can be found in

17. Movement of mood

18. Dow Jones and Calm: correlated gaph

19. Test Results
The test show the up and down of stock market at the prediction accuracy of 86.7%.