1. Chapter 2
Data Mining Tasks

2. Data Mining Tasks Prediction methods
Use some variables to predict unknown or future values of the same or other variables.
Inference on the current data in order to make prediction
Description methods
Find human interpretable patterns that describe data
Characterize the general properties of data in db
Descriptive mining is complementary to predictive mining but it is closer to decision support than decision making

3. Cont’d Association Rule Mining (descriptive)
Classification and Prediction (predictive)
Clustering (descriptive)
Sequential Pattern Discover (descriptive)
Regression (predictive)
Deviation Detection (predictive)

4. Association Rule Mining
Initially developed for market basket analysis
Goal is to discover relationships between attributes
Data is typically stored in very large databases, sometimes in flat files or images
Uses include decision support, classification and clustering
Application areas include business, medicine and engineering

5. Association Rule Mining
Given a set of transactions, each of which is a set of items, find all rules (XY) that satisfy user specified minimum support and confidence constraints
Support = (#T containing X and Y)/(#T)
Confidence=(#T containing X and Y)/ (#T containing X)
Applications
Cross selling and up selling
Supermarket shelf management
Some rules discovered
Bread Jem
Sup=60%, conf=75%
Jelly Bread
Sup=60%, conf=100%
Jelly Jem
Sup=20%, conf=100%
Jelly Milk
Sup=0%

6. Association Rule Mining: Definition
Given a set of records, each of which contain some number of items from a given collection:
Produce dependency rules which will predict occurrence of an item based on occurrences of other items
Example:
{Bread} {Jem}
{Jelly} {Jem}

7. Association Rule Mining: Marketing and sales promotion
Say the rule discovered is
{Bread, …} {Jem}
Jem as a consequent: can be used to determine what products will boost its sales.
Bread as antecedent: can be used to see which products will be impacted if the store stops selling bread
Bread as an antecedent and Jem as a consequent: can be used to see what products should be stocked along with Bread to promote the sale of Jem.

8. Association Rule Mining: Supermarket shelf management
Goal: To identify items that are bought concomitantly by a reasonable fraction of customers so that they can be shelved.
Data Used: Point-of sale data collected with barcode scanners to find dependencies among products.
Example
If customer buys jelly, then he is very likely to by Jem.
So don’t be surprised if you find Jem next to Jelly on an aisle in the super market. Also salsa next to tortilla chips.

9. Association Rule Mining
Association rule mining will produce LOTS of rules
How can you tell which ones are important?
High Support
High Confidence
Rules involving certain attributes of interest
Rules with a specific structure
Rules with support / confidence higher than expected
Completeness – Generating all interesting rules
Efficiency – Generating only rules that are interesting

10. Clustering
Determine object groupings such that objects within the same cluster are similar to each other, while objects in different groups are not
Typically objects are represented by data points in a multidimensional space with each dimension corresponding to one or more attributes. Clustering problem in this case reduces to the following:
Given a set of data points, each having a set of attributes, and a similarity measure, find cluster such that
Data points in one cluster are more similar to one another
Data points in separate clusters are less similar to one another

11. Cont’d Similarity measures: Types of Clustering
Euclidean distance (continuous attr.)
Other problem – specific measures
Types of Clustering
Group-Based Clustering
Hierarchical Clustering

12. Clustering Example Euclidean distance based clustering in 3D space
Intra cluster distances are minimised
Inter cluster distances are maximised

13. Clustering: Market Segmentation
Goal: To subdivide a market into distinct subset of customers where each subset can be targeted with a distinct marketing mix
Approach:
Collect different attributes of customers based on their geographical and lifestyle related information
Find clusters of similar customers
Measure the clustering quality by observing the buying patterns of customers in the same cluster vs. those from different clusters.

14. Clustering: Document Clustering
Goal: To find groups of documents that are similar to each other based on important terms appearing in them
Approach: To identify frequently occurring terms in each document. Form a similarity measure based on frequencies of different terms. Use it to generate clusters.
Gain: Information Retrieval can utilize the clusters to relate a new document or search to clustered documents

15. Clustering: Document Clustering Example
Clustering points: 3204 articles of LA Times
Similarity measure: Number of common words in documents (after some word filtering)

16. Classification: Definition
Given a set of records (called the training set)
Each record contains a set of attributes. One of the attributes is the class
Find a model for the class attribute as a function of the values of other attributes
Goal: Previous unseen records should be assigned to a class as accurately as possible
Usually, the given data set is divided into training and test set, with training set used to build the model and test set used to validate it. The accuracy of the model is determined on the test set.

17. Classification: cont’d
Classifiers are created using labeled training samples
Classifiers are evaluated using independent labeled samples (test set)
Training samples created by ground truth / experts
Classifier later used to classify unknown samples
Measurements must be able to predict the phenomenon!
Examples
Direct marketing
Fraud detection
Customer churn
Sky survey cataloging
Classifying galaxies

18. Classification Example

19. Classification: Direct Marketing
Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell phone product
Approach:
Use the data collected for a similar product introduced in the recent past.
Use the profiles of consumers along with their (buy, didn’t buy} decision. The latter becomes the class attribute.
The profile of the information may consist of demographic, lifestyle and company interaction.
Demographic – Age, Gender, Geography, Salary
Psychographic - Hobbies
Company Interaction – Recentness, Frequency, Monetary
Use these information as input attributes to learn a classifier model

20. Classification: Fraud Detection
Goal: Predict fraudulent cases in credit card transactions
Approach:
Use credit card transactions and the information on its account holders as attributes (important: when and where the card was used)
Label past transactions as {fraud, fair} transactions. This forms the class attribute
Learn a model for the class of transactions
Use this model to detect fraud by observing credit card transactions on an account.

21. Regression
Predict the value of a given continuous valued variable based on the values of other variables, assuming a linear or non-linear model of dependency
Extensively studied in the fields of Statistics and Neural Networks
Predicting sales number of new product based on advertising expenditure
Predicting wind velocities based on temperature, humidity, air pressure, etc
Time series prediction of stock market indices

22. Deviation/Anomaly Detection
Some data objects do not comply with the general behavior or model of the data. Data objects that are different from or inconsistent with the remaining set are called outliers
Outliers can be caused by measurement or execution error. Or they represent some kind of fraudulent activity
Goal of deviation/anomaly detection is to detect significant deviations from normal behavior

23. Deviation/Anomaly Detection: Definition
Given a set of n points or objects, and k, the expected number of outliers, find the top k objects that considerably dissimilar, exceptional or inconsistent with the remaining data
This can be viewed as two sub problems
Define what data can be considered as inconsistent in a given data set
Find an efficient method to mine the outliers

24. Deviation: Credit Card Fraud Detection
Goal: to detect fraudulent credit card transactions
Approach:
Based on past usage patterns, develop model for authorized credit card transactions
Check for deviation from model, before authenticating new credit card transactions
Hold payment and verify authenticity of “doubtful” transaction by other means (phone call, etc.)

25. Anomaly detection: Network Intrusion Detection
Goal: to detect intrusion of a computer network
Approach:
Define and develop a model for normal user behavior on the computer network
Continuously monitor behavior of users to check if it deviates from the defined normal behavior
Raise an alarm, if such deviation is found

26. Sequential pattern discovery: definition
Given is a set of objects, with each object associated with its own time of events, find rules that predict strong sequential dependencies among different events
Sequence discovery aims at extracting sets of events that commonly occur over a period of time
(A B) (C)  (D E)

27. Sequential pattern discovery: Telecommunication Alarm Logs
(Inverter_Problem Excessive_Line_Current) (Rectifier_Alarm)  (Fire_Alarm)

28. Sequential pattern discovery: Point of Sell Up Sell / Cross Sell
Point of sale transaction sequences
Computer bookstore
(Intro_to_Visual_C) (C++ Primer)  (Perl_For_Dummies, Tcl_Tk)
60% customers who buy Intro toVisual C and C++ Primer also buy Perl for dummies and Tcl Tk within a month
Athletic apparel store
(Shoes) (Racket, Racket ball)  (Sport_Jacket)

29. Example: Data Mining(Weather data)
By applying various data mining techniques, we can find
associations and regularities in our data
Extract knowledge in the forms of rules, decision trees etc.
Predict the value of the dependent variable in new situation
Some example
Mining association rules
Classification by decision trees and rules
Prediction methods

30. Mining association rules
First, discretize the numeric attributes (a part of the data preprocessing stage)
Group the temperature values in three intervals (hot, mild, cool) and humidity values in two (high, normal)
Substitute the values in data with the corresponding names
Apply the Apriori algorithm and get the following rules

31. Discretized weather data
Day
outlook
temperature
humidity
windy
play 1
sunny
hot
high
false No 2
true 3
overcast
False
Yes 4
rainy
mild 5
cool
normal 6
True 7 8 9 10 11 12 13 14 no

32. Cont’d humidity=normal windy=false  play=yes (4,1)
temperature=cool  humidity=normal (4,1)
outlook=overcast  play=yes (4,1)
temperature=cool play=yes  humidity=normal (3,1)
outlook=rainy windy=false  play=yes (3, 1)
outlook=rainy play=yes  windy=false (3, 1)
outlook=sunny humidity=high  play=no (3, 1)
outlook=sunny play=no  humidity=high (3, 1)
temperature=cool windy=false  humidity=normal play=yes (2, 1)
temperature=cool humidity=normal windy=false  play=yes (2, 1)

33. Cont’d
These rules show some attribute values sets (itemsets) that appear frequently in the data
Support (the number of occurrences of the itemset in the data)
Confidence (accuracy) of the rules
Rule 3 – the same as the one that is produced by observing the data cube

34. Classification by Decision Trees and Rules
Using ID3 algorithm, the following decision tree is produced
Outlook=sunny
Humidity=high:no
Humidity=normal:yes
Outlook=overcast:yes
Outlook=rainy
Windy=true:no
Windy=false:yes

35. Cont’d Decision tree consists of:
Decision nodes that test the values of their corresponding attribute
Each value of this attribute leads to a subtree and so on, until the leaves of the tree are reached
They determine the value of the dependent variable
Using a decision tree we can classify new tuples

36. Cont’d A decision tree can be presented as a set of rules
Each rule represents a path through the tree from the root to a leaf
Other data mining techniques can produce rules directly: Prism algorithm
if outlook=overcast then yes
if humidity=normal and windy=false then yes
If temperature=mild and humidity=normal the yes
If outlook=rainy and windy=false then yes
If outlook=sunny and humidity=high then no
If outlook=rainy and windy=true then no

37. Prediction methods
DM offers techniques to predict the value of the dependent variable directly without first generating a model
The most popular approaches is based of statistical methods
Uses the Bayes rule to predict the probability of each value of the dependent variable given the values of the independent variables

38. Cont’d Eg: applying Bayes to the new tuple:
(sunny, mild, normal, false, ?)
P(play=yes| outlook=sunny, temperature=mild, humidity=normal, windy=false) = 0.8
P(play=no| outlook=sunny, temperature=mild, humidity=normal, windy=false) = 0.2
 The predicted value must be “yes”

39. Data Mining : Problems and Challenges
Noisy
data
Large Databases
Dynamic Databases
Difficult Training Set
Incomplete Data

40. Noisy data many of attribute values will be inexact or incorrect
erroneous instruments measuring some property
human errors occurring at data entry
two forms of noise in the data
corrupted values - some of the values in the training set are altered from the original form
missing values - one or more of the attribute values may be missing both for examples in the training set and for object which are to be classified.

41. Difficult Training Set
Non-representative data
Learning are based on a few examples
Using large db, the rules probably representative
Absence of boundary cases
To find the real differences between two classes
Limited information
Two objects to be classified give the same conditional attributes but are classified in the diff class
Not have enough information of distinguishing two types of objects

42. Dynamic databases Db change continually
Rules that reflect the content of the db at all time (preferred)
If same changes are made, the whole learning process may have to be conducted again

43. Large databases The size of db to be ever increasing
Machine learning algorithms – handling a small training set (a few hundred examples)
Much care on using similar techniques in larger db
Large db – provide more knowledge (eg. rules may be enormous)

44. Data Mining – Issues in Data Mining
User Interaction / Visualization
Incorporation of Background Knowledge
Noisy or Incomplete Data
Determining Interestingness of Patterns
Efficiency and Scalability
Parallel and Distributed Mining
Incremental Learning / Mining Time-Changing Phenomena
Mining from Image / Video / Audio Data
Mining Unstructured Data