1. DATAWAREHOUSING AND DATAMINING
UNIT V
DATAWAREHOUSING AND DATAMINING

2. Steps of Data Mining

3. Steps of Data Mining
There are various steps that are involved in mining data Data Integration: First of all the data are collected and integrated from all the different sources.
Data Selection: We may not all the data we have collected in the first step. So in this step we select only those data which we think useful for data mining.
Data Cleaning: The data we have collected are not clean and may contain errors, missing values, noisy or inconsistent data. So we need to apply different techniques to get rid of such anomalies.
Data Transformation: The data even after cleaning are not ready for mining as we need to transform them into forms appropriate for mining. The techniques used to accomplish this are smoothing, aggregation, normalization etc.
Data Mining: Now we are ready to apply data mining techniques on the data to discover the interesting patterns. Techniques like clustering and association analysis are among the many different techniques used for data mining.
Pattern Evaluation and Knowledge Presentation: This step involves visualization, transformation, removing redundant patterns etc from the patterns we generated.
Decisions / Use of Discovered Knowledge: This step helps user to make use of the knowledge acquired to take better decisions.

4. Data warehouse
DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise.
Examples of reports could range from annual and quarterly comparisons and trends to detailed daily sales analysis.

5. Data Mining for Customer Modeling
Customer Tasks:
attrition prediction
targeted marketing:
cross-sell, customer acquisition
credit-risk
fraud detection
Industries
banking, telecom, retail sales, …

6. Customer Attrition: Case Study
Situation: Attrition rate at for mobile phone customers is around 25-30% a year!
Task:
Given customer information for the past N months, predict who is likely to attrite next month.
Also, estimate customer value and what is the cost-effective offer to be made to this customer.

7. Customer Attrition Results
Verizon Wireless built a customer data warehouse
Identified potential attriters
Developed multiple, regional models
Targeted customers with high propensity to accept the offer
Reduced attrition rate from over 2%/month to under 1.5%/month (huge impact, with >30 M subscribers)

8. Assessing Credit Risk: Case Study
Situation: Person applies for a loan
Task: Should a bank approve the loan?
Note: People who have the best credit don’t need the loans, and people with worst credit are not likely to repay. Bank’s best customers are in the middle

9. Credit Risk - Results
Banks develop credit models using variety of machine learning methods.
Mortgage and credit card proliferation are the results of being able to successfully predict if a person is likely to default on a loan
Widely deployed in many countries

10. Successful e-commerce – Case Study
A person buys a book (product) at Amazon.com.
Task: Recommend other books (products) this person is likely to buy
Amazon does clustering based on books bought:
customers who bought “Advances in Knowledge Discovery and Data Mining”, also bought “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”
Recommendation program is quite successful

11. Major Data Mining Tasks
Classification: predicting an item class
Clustering: finding clusters in data
Associations: e.g. A & B & C occur frequently

12. Classification
Learn a method for predicting the instance class from pre-labeled (classified) instances
It is also called as supervised learning
Many approaches: Regression,
Decision Trees,
Bayesian,
Neural Networks,
...
Given a set of points from classes
what is the class of new point ?

13. Supervised vs. Unsupervised Learning
Supervised learning (classification)
Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
New data is classified based on the training set
Unsupervised learning (clustering)
The class labels of training data is unknown
Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data

14. Classification: Decision Trees
if X > 5 then blue
else if Y > 3 then blue
else if X > 2 then green
else blue Y 3 2 5 X

15. Example : The weather problem
Outlook
Temperature
Humidity
Windy
Play
sunny 85
false no 80 90
true
overcast 83 86
yes
rainy 70 96 68 65 64 72 95 69 75 81 71 91
Given past data,
Can you come up
with the rules for
Play/Not Play ?
What is the game?

16. The weather problem Conditions for playing
Outlook
Temperature
Humidity
Windy
Play
Sunny
Hot
High
False No
True
Overcast
Yes
Rainy
Mild
Normal …
If outlook = sunny and humidity = high then play = no
If outlook = rainy and windy = true then play = no
If outlook = overcast then play = yes
If humidity = normal then play = yes
If none of the above then play = yes

17. Weather data with mixed attributes
Some attributes have numeric values
Outlook
Temperature
Humidity
Windy
Play
Sunny 85
False No 80 90
True
Overcast 83 86
Yes
Rainy 75 …
If outlook = sunny and humidity > 83 then play = no
If outlook = rainy and windy = true then play = no
If outlook = overcast then play = yes
If humidity < 85 then play = yes
If none of the above then play = yes

18. A decision tree for this problem
outlook
rainy
sunny
overcast
humidity
yes
windy
FALSE
TRUE
high
normal
yes no no
yes

19. Attribute Selection Splitting Criteria
determines the best way to split
Measures
Information Gain
Gain Ratio
Gini Index

20. Which attribute to select?

21. A criterion for attribute selection
Which is the best attribute?
The one which will result in the smallest tree
Heuristic: choose the attribute that produces the “purest” nodes
Popular impurity criterion: information gain
Information gain increases with the average purity of the subsets that an attribute produces
Strategy: choose attribute that results in greatest information gain
witten&eibe

22. Association Analysis Discovery of Association Rules
showing attribute-value conditions that occur frequently together in a set of data, e.g. market basket
Given a set of data, find rules that will predict the occurrence of a data item based on the occurrences of other items in the data
A rule has the form body ⇒head
buys(Omar, “milk”) ⇒ buys(Omar, “sugar”)

23. Association Analysis

24. Association Analysis Location Business Type 1
Barber, Bakery, Convenience Store, Meat Shop, Fast Food 2
Bakery, Bookstore, Petrol Pump, Convenience Store, Library, Fast Food 3
Carpenter, Electrician, Barber, Hardware Store, 4
Bakery, Vegetable Market, Flower Shop, Sweets Shop, Meat Shop 5
Convenience Store, Hospital, Pharmacy, Sports Shop, Gym, Fast Food 6
Internet Café, Gym, Games Shop, Shorts Shop, Fast Food, Bakery
Association Rule: X Y ; (Fast Food, Bakery)  (Convenience Store)
Support S: Fraction of items that contain both X and Y = P(X U Y)
S(Fast Food, Bakery, Convenience Store) = 2/6 = .33
Confidence C: how often items in Y appear in locations that contain X = P(X U Y)
C[(Fast Food, Bakery)  (Convenience Store)] = P(X U Y) / P(X)
= 0.33/0.50 = .66

25. Association Analysis
Given a set of transactions T, the goal of association rule mining is to find all rules having
support ≥ minsup threshold
confidence ≥ minconf threshold
Brute-force approach:
List all possible association rules
Compute the support and confidence for each rule
Prune rules that fail the minsup and minconf thresholds
⇒ Computationally prohibitive!

26. Association Analysis Location Business Type 1
Barber, Bakery, Convenience Store, Meat Shop, Fast Food, Meat Shop 2
Bakery, Bookstore, Petrol Pump, Convenience Store, Library, Fast Food 3
Carpenter, Electrician, Barber, Hardware Store, Meat Shop 4
Bakery, Vegetable Market, Flower Shop, Sweets Shop, Meat Shop 5
Convenience Store, Hospital, Pharmacy, Sports Shop, Gym, Fast Food 6
Internet Café, Gym, Sweets Shop, Shorts Shop, Fast Food, Bakery
Association Rules:
(Fast Food, Bakery)  (Convenience Store) Support S: .33 Confidence C: .66
(Convenience Store, Bakery)  (Fast Food) Support S: .33 Confidence C: .50
(Fast Food, Convenience Store)  (Bakery) Support S: .33 Confidence C: .50
(Convenience Store)  (Fast Food, Bakery) Support S: .33 Confidence C: .66
(Fast Food)  (Convenience Store, Bakery) Support S: .33 Confidence C: 1
(Bakery)  (Fast Food, Convenience Store) Support S: .33 Confidence C: .66

27. Association Analysis Observations
Association Rules:
(Fast Food, Bakery)  (Convenience Store) Support S: .33 Confidence C: .66
(Convenience Store, Bakery)  (Fast Food) Support S: .33 Confidence C: .50
(Fast Food, Convenience Store)  (Bakery) Support S: .33 Confidence C: .50
(Convenience Store)  (Fast Food, Bakery) Support S: .33 Confidence C: .66
(Fast Food)  (Convenience Store, Bakery) Support S: .33 Confidence C: 1
(Bakery)  (Fast Food, Convenience Store) Support S: .33 Confidence C: .66
Observations
 Above rules are binary partitions of given item set
 Identical Support but different Confidence
 Support and Confidence thresholds may be different

28. Reducing the number of candidatesL1
Scan 1
Business Type
Count
Barber 2
Bakery
Book tore 1
Carpenter
Convenience Store 3
Electrician
Fast Food
Flower Shop
Gym
Games Shop
Hardware Store
Hospital
Internet Café
Library
Meat Shop
Petrol Pump
Pharmacy
Sports Shop
Sweets Shop
Vegetable Market
Filter Minimum Occurrences
m < 2
Business Type
Count
Barber 2
Bakery
Convenience Store 3
Fast Food
Filter
Pairs of Two Items; 4C2 = 6 L2
Business Type
Count
(Barber, Bakery) 1
(Barber, Convenience Store)
(Barber, Fast Food)
(Bakery, Convenience Store) 2
(Bakery, Fast Food) 3
(Convenience Store, Fast Food)
Filter Minimum Occurrences
m < 2
Business Type
Count
(Bakery, Convenience Store) 2
(Bakery, Fast Food) 3
(Convenience Store, Fast Food)

29. Classification vs Association Rules
Classification Rules
Focus on one target field
Specify class in all cases
Measures: Accuracy
Association Rules
Many target fields
Applicable in some cases
Measures: Support, Confidence, Lift

30. CLUSTERING 1. A cluster is a subset of objects which are “similar”
2. A subset of objects such that the distance between any two objects in the cluster is less than the distance between any object in the cluster and any object not located inside it.
3. A connected region of a multidimensional space containing a relatively high density of objects.
Unsupervised learning:
Finds “natural” grouping of instances given unlabeled data

31. K means clustering algorithm
1. Initialize the center of the clusters
2. Attribute the closest cluster to each data point
3. Set the position of each cluster to the mean of all data points belonging to that cluster
4. Repeat steps 2-3 until convergence
Notation