1. Intelligence Through Learning from Data
Monash University
Semester 1, March 2006

2. Lecture Outline
Machine Learning – Yet another form of intelligent software
Learning for Data
Data Mining – A real world application of learning from data
Data Mining Concepts
Data Mining Techniques
Data Mining Applications

3. Lecture Objectives By the end of this lecture, you should:
Understand the relationship between machine learning and data mining
Know the principles of learning from data and the various techniques for learning from data
Understand the real world applications of learning from data
Be able to distinguish between this form of intelligence in software systems and other strategies such as software agents, context-awareness, expert systems and knowledge representation/deductive approaches
Why association rules - business needs to identify the relationships
Machine learning – effective data mining requires learning.

4. Machine Learning

5. Machine Learning
Machine Learning is an area of Artificial Intelligence.
It is concerned with programs that learn
Data Mining uses machine learning for prediction and classification
Feedback on the correctness of a prediction combined with examples and domain knowledge allow the program to learn.
Machine Learning is also used in speech recognition, robot training, classification of astronomical structures and game playing.

6. Machine Learning
“A general law can never be verified by a finite number of observations. It can, however, be falsified by only one observation.” Karl Popper
The patterns that machine learning algorithms find can never be definitive theories
Any results discovered must to be tested for statistical relevance

7. The Empirical Cycle
Analysis
Theory
Observation
Prediction

8. Concept Learning - 1 Example: the concept of a wombat
a learning algorithm could consider many animals and be advised in each case whether it is a wombat or not. From this a definition would be deduced.
The definition is
complete if it recognizes all instances of a concept ( in this case a wombat).
consistent if it does not classify any negative examples as falling under the concept.

9. Concept Learning - 2
An incomplete definition is too narrow and would not recognize some wombats.
An inconsistent definition is too broad and would classify some non-wombats as wombats.
A bad definition could be both inconsistent and incomplete.

10. Hypothesis Characteristics - 1
Classification Accuracy
1 in a million wrong is better than 1 in 10 wrong.
Transparency
A person is able understand the hypothesis generated. It is then much easier to take action

11. Hypothesis Characteristics - 2
Statistical Significance
The hypothesis must perform better than the naïve prediction. (Imagine if 80% of animals considered are wombats and the theory is that all animals are wombats then the theory is right 80% of the time! But nothing has been learnt.)
Information Content
We look for a rich hypothesis. The more information contained (while still being transparent) the more understanding is gained and the easier it is to formulate an action plan.

12. Complexity of Search Space
Machine learning can be considered as a search problem. We wish to find the correct hypothesis from among many.
If there are only a few hypotheses we could try them all but if there are an infinite number we need a better strategy.
If we have a measure of the quality of the hypothesis we can use that measure to select potential good hypotheses and based on the selection try to improve the theories (hill-climbing search)
Consider the metaphor of the kangaroo in the mist.
This demonstrates that it is important to know the complexity of the search space. Also that some pattern recognition patterns are almost impossible to solve.

13. Learning as a Compression
We have learnt something if we have an algorithm that creates a description of the data that is shorter than the original data set
A knowledge representation is required that is incrementally compressible and an algorithm that can achieve that incremental compression
The file-in could be a relation table and the file-out a prediction or a suggested clustering
File-out
Algorithm
File-in

14. Data Mining

15. Introduction Motivation: Why data mining? What is data mining?
Data Mining: On what kind of data?
Data mining functionality
Are all the patterns interesting?
Classification of data mining systems
Link to Data Warehousing

16. Motivation: “Necessity is the Mother of Invention”
Data explosion problem
Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories
We are drowning in data, but starving for knowledge!
Solution: Data warehousing and data mining
Data warehousing and on-line analytical processing
Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases

17. Evolution of Database Technology
Data collection, database creation, IMS and network DBMS
1970s:
Relational data model, relational DBMS implementation
1980s:
RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)
1990s—2000s:
Data mining and data warehousing, multimedia databases, and Web databases

18. What Is Data Mining?
Data mining (knowledge discovery in databases - KDD):
Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases
Alternative names and their “inside stories”:
Data mining: a misnomer?
Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.
What is not data mining?
(Deductive) query processing.
Expert systems or small ML/statistical programs

19. Rules 1% support If test A then test B
Episodes Database
GP Database
Data Preparation
Merge
Association Discovery
Database Segmentation
Rules 1% support If test A then test B
will occur in 62%
of cases
Segment 1 Segment 2
97 GPs GPs
Score = 1.8 Score = 2.7

20. Why Data Mining? — Potential Applications
Database analysis and decision support
Market analysis and management
target marketing, customer relation management, market basket analysis, cross selling, market segmentation
Risk analysis and management
Forecasting, customer retention, improved underwriting, quality control, competitive analysis
Fraud detection and management
Other Applications
Text mining (news group, , documents) and Web analysis.
Intelligent query answering

21. Market Analysis and Management (1)
Where are the data sources for analysis?
Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies
Target marketing
Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.
Determine customer purchasing patterns over time
Conversion of single to a joint bank account: marriage, etc.
Cross-market analysis
Associations/co-relations between product sales
Prediction based on the association information

22. Market Analysis and Management (2)
Customer profiling
data mining can tell you what types of customers buy what products (clustering or classification)
Identifying customer requirements
identifying the best products for different customers
use prediction to find what factors will attract new customers
Provides summary information
various multidimensional summary reports
statistical summary information (data central tendency and variation)

23. Corporate Analysis and Risk Management
Finance planning and asset evaluation
cash flow analysis and prediction
contingent claim analysis to evaluate assets
cross-sectional and time series analysis (financial-ratio, trend analysis, etc.)
Resource planning:
summarize and compare the resources and spending
Competition:
monitor competitors and market directions
group customers into classes and a class-based pricing procedure
set pricing strategy in a highly competitive market

24. Fraud Detection and Management (1)
Applications
widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc.
Approach
use historical data to build models of fraudulent behavior and use data mining to help identify similar instances
Examples
auto insurance: detect a group of people who stage accidents to collect on insurance
money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network)
medical insurance: detect professional patients and ring of doctors and ring of references

25. Fraud Detection and Management (2)
Detecting inappropriate medical treatment
Health Insurance Commission identifies that in many cases blanket screening tests might have been requested (can save $$).
Detecting telephone fraud
Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm.
British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud.
Retail
Analysts estimate that 38% of retail shrink is due to dishonest employees.

26. Other Applications Sports
IBM Advanced Scout analyzed NBA game statistics (shots blocked, assists, and fouls) to gain competitive advantage for New York Knicks and Miami Heat
Astronomy
JPL and the Palomar Observatory discovered 22 quasars with the help of data mining
Internet Web Surf-Aid
IBM Surf-Aid applies data mining algorithms to Web access logs for market-related pages to discover customer preference and behavior pages, analyzing effectiveness of Web marketing, improving Web site organization, etc.

27. Data Mining: A KDD Process
Knowledge
Data mining: the core of knowledge discovery process.
Pattern Evaluation
Data Mining
Task-relevant Data
Data Warehouse
Selection
Data Cleaning
Data Integration
Databases

28. The Process of Knowledge Discovery
Reporting
Data
Cleaning & Enrichment
Coding
Data mining
selection
- clustering
-domain consistency
- segmentation
-de-duplication
- prediction
-disambiguation
Information
Requirement
Action
Feedback
Operational data
External data
The Knowledge Discovery in Databases (KDD) process (Adriens/Zantinge)

29. Steps of a KDD Process Learning the application domain:
relevant prior knowledge and goals of application
Creating a target data set: data selection
Data cleaning and preprocessing: (may take 60% of effort!)
Data reduction and transformation:
Find useful features, dimensionality/variable reduction, invariant representation.
Choosing functions of data mining
summarization, classification, regression, association, clustering.
Choosing the mining algorithm(s)
Data mining: search for patterns of interest
Pattern evaluation and knowledge presentation
visualization, transformation, removing redundant patterns, etc.
Use of discovered knowledge

30. Data Mining and Business Intelligence
Increasing potential
to support
business decisions
Making
Decisions
End User
Data Presentation
Business
Analyst
Visualization Techniques
Data Mining
Information Discovery
Data
Analyst
Data Exploration
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
OLAP, MDA
Data Sources
DBA
Paper, Files, Information Providers, Database Systems, OLTP

31. Architecture of a Typical Data Mining System
Graphical user interface
Pattern evaluation
Data mining engine
Knowledge-base
Database or data warehouse server
Filtering
Data cleaning & data integration
Data
Warehouse
Databases

32. Data Mining: On What Kind of Data?
Relational databases
Data warehouses
Transactional databases
Advanced DB and information repositories
Object-oriented and object-relational databases
Spatial databases
Time-series data and temporal data
Text databases and multimedia databases
Heterogeneous and legacy databases
WWW

33. Data Mining Techniques
Various taxonomies exist. Berry & Linoff define 6 tasks
Classification
Estimation
Prediction
Clustering
Description
Affinity Grouping
Cabena et al. define 4 operations(i.e. tasks)
Predictive Modeling
Database Segmentation
Link Analysis
Deviation Detection
Briefly we will look at the techniques and we will talk about one of them, how is done in detail in subsequent lectures. However it is worth while to know the taxonomy.
Taxonomy can be done based on the principles used in mining properties of data or pre-conceived strategy of data mining.

34. Classification
Classification involves considering the features of some object then assigning it it to some pre-defined class, for example:
Spotting fraudulent insurance claims
Which phone numbers are fax numbers
Which customers are high-value
Basically partition the data into groups so that the elements of each group has some common properties.
Usually the properties are based on the features or derivative of the attributes of the objects
For example, in the case of fraudulent data, may the withdrawn rate (time interval between the withdrawal) and/or the amount may constitute the feature for classification.

35. Estimation
Estimation deals with numerically valued outcomes rather than discrete categories as occurs in classification.
Estimating the number of children in a family
Estimating family income
Estimation – primarily applied to numerical data rather than categorical data
Estimation is determine a value for an unknown output attribute.
Most of the supervised learning algorithms can perform classification and estimation
We say a method is supervised learning when we know the dependent variable to guide the data mining (learning) process.
For example, the classification is supervised learning because we know the attributes that we will use in defining the classes.
On the contrary unsupervised leaning we are without a dependent variable.

36. Prediction
Essentially the same as classification and estimation but involves future behaviour
Historical data is used to build a model explaining behaviour (outputs) for known inputs
The model developed is then applied to current inputs to predict future outputs
Predict which customers will respond to a promotion
Classifying loan applications
Difficult to differentiate between classification and estimation.
Purpose of prediction is to determine future outcome rather than current behaviour.
The output variable of predictive model can be numeric or categorical.

37. Clustering
Clustering is also sometimes referred to as segmentation (though this has other meanings in other fields)
In clustering there are no pre-defined classes. Self-similarity is used to group records. The user must attach meaning to the clusters formed
Clustering often precedes some other data mining task, for example:
once customers are separated into clusters, a promotion might be carried out based on market basket analysis of the resulting cluster
Clustering can be supervised or unsupervised.
In the case of unsupervised clustering – is to discover concept structures in data.

38. Description
A good description of data can provide understanding of behaviour
The description of the behaviour can suggest an explanation for it as well
Statistical measures can be useful in describing data, as can techniques that generate rules

39. Deviation Detection
Records whose attributes deviate from the norm by significant amounts are also called outliers
Application areas include:
fraud detection
quality control
tracing defects.
Visualization techniques and statistical techniques are useful in finding outliers
A cluster which contains only a few records may in fact represent outliers
Outliers – quite useful when you want to identify unusual or non-standard data.
Visualisation techniques are quite useful here as the outlier data will stand out from other data.
Or clustering where the size of the cluster may give some clue.

40. Affinity Grouping
Affinity grouping is also referred to as Market Basket Analysis
A common example is the discovery of which items are frequently sold together at a supermarket. If this is known, decisions can be made about:
arranging items on shelves
which items should be promoted together
which items should not simultaneously be discounted
Also referred as relationship analysis – but popularly known as market basket analysis because of its use in retail business applications.
Once you identify the relationships, various strategies can be used to exploit the relationship to the benefit of the business.

41. Association Rule Mining
Confidence
Rule Body
When a customer buys a shirt, in 70% of cases,
he or she will also buy a tie!
We find this happens in 13.5% of all purchases.
How do you specify the relationship – by means of rules (also called association rules)
Association rule has a number of components  in simple case there will be a LHS and RHS and they are related.
Formally expressed as IF … THEN … rule.
Rule Head
Support

42. Association Rule Mining
Some rules are useful: Unknown, unexpected and indicative of some action to take.
Some rules are trivial: Known by anyone familiar with the business.
Some rules are inexplicable: Seem to have no explanation and do not suggest a course of action. “The key to success in business is to know something that nobody else knows” Aristotle Onassis

43. Co-Occurrence Table Customer Items 1 orange juice (OJ), cola
2 milk, orange juice, window cleaner
3 orange juice, detergent
4 orange juice, detergent, cola
5 window cleaner, cola
OJ Cleaner Milk Cola Detergent OJ
Cleaner
Milk
Cola
Detergent
Let me explain by an example before we formalise the association rule.

44. From the Co-Occurrence Table
We can say that people who buys Orange Juice also will buy Cola ( or detergent) orange juice  cola
This association rule is satisfied by 2 out of 5 customers ( 1 and 4) hence support is 2/5 = 40%
However, there are three customers (1,3 and 4) have purchased orange juice and hence the confidence of the above rule is only 2/3 = 66.67%
Question: Are support and confidence measures good enough?
The rule has one item (or attribute) on the left hand side and the right hand side. How do you find rules which has more than one items on the left hand side (multi-attribute rule)
What it means when the support is 100% - an obvious rule of no significance to the business.
Some support but low confidence!!
Low support but high confidence?

45. Support and Confidence
Percentage of transactions from a transaction database that the given rule satisfies.
This can be taken as the probability P(X  Y) where X  Y indicates that a transaction contains both X and Y, that is union of item sets X and Y.
Confidence:
Which assess the degree of certainty of the detected association.
This can be taken as the conditional probability P(Y|X), that is, the probability that a transaction containing X also contains Y.
More formally
Support (X  Y ) = P (X  Y)
Confidence (X  Y) = P (Y|X)

46. What is a Rule? If condition then result Note:
If nappies and Thursday then beer
is usually better than (in the sense that it is more actionable)
If Thursday then nappies and beer because it has just one item in the result
If a 3 way combination is the most common, then consider rules with just 1 item in the result, e.g.
If A and B, then C
If A and C, then B
So far we talk rule of one LHS and one RHS.
It does not matter how many attributes we have on RHS, they can be broken into canonical form.
What about more than attribute on LHS?

47. Data Mining Functionalities (2)
Classification and Prediction
Finding models (functions) that describe and distinguish classes or concepts for future prediction
E.g., classify countries based on climate, or classify cars based on gas mileage
Presentation: decision-tree, classification rule, neural network
Prediction: Predict some unknown or missing numerical values
Cluster analysis
Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns
Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity

48. Data Mining Functionalities (3)
Outlier analysis
Outlier: a data object that does not comply with the general behavior of the data
It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis
Trend and evolution analysis
Trend and deviation: regression analysis
Sequential pattern mining, periodicity analysis
Similarity-based analysis
Other pattern-directed or statistical analyses

49. Are All the “Discovered” Patterns Interesting?
A data mining system/query may generate thousands of patterns, not all of them are interesting.
Suggested approach: Human-centered, query-based, focused mining
Interestingness measures: A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm
Objective vs. subjective interestingness measures:
Objective: based on statistics and structures of patterns, e.g., support, confidence, etc.
Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.

50. Can We Find All and Only Interesting Patterns?
Find all the interesting patterns: Completeness
Can a data mining system find all the interesting patterns?
Association vs. classification vs. clustering
Search for only interesting patterns: Optimization
Can a data mining system find only the interesting patterns?
Approaches
First general all the patterns and then filter out the uninteresting ones.
Generate only the interesting patterns—mining query optimization

51. Data Mining: Confluence of Multiple Disciplines
Database
Technology
Statistics
Data Mining
Machine
Learning
Visualization
Information
Science
Other
Disciplines

52. Data Mining: Classification Schemes
General functionality
Descriptive data mining
Predictive data mining
Different views, different classifications
Kinds of databases to be mined
Kinds of knowledge to be discovered
Kinds of techniques utilized
Kinds of applications adapted

53. A Multi-Dimensional View of Data Mining Classification
Databases to be mined
Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc.
Knowledge to be mined
Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc.
Multiple/integrated functions and mining at multiple levels
Techniques utilized
Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, neural network, etc.
Applications adapted
Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc.

54. Data Mining and the Data Warehouse
Organizations realized that they had large amounts of data stored (especially of transactions) but it was not easily accessible
The data warehouse provides a convenient data source for data mining. Some data cleaning has usually occurred. It exists independently of the operational systems
Data is retrieved rather than updated
Indexed for efficient retrieval
Data will often cover 5 to 10 years
A data warehouse is not a pre-requisite for data mining

55. Data Mining and OLAP Online Analytic Processing (OLAP)
Tools that allow a powerful and efficient representation of the data
Makes use of a representation known as a cube
A cube can be sliced and diced
OLAP provide reporting with aggregation and summary information but does not reveal patterns, which is the purpose of data mining

56. Major Issues in Data Mining (1)
Mining methodology and user interaction
Mining different kinds of knowledge in databases
Interactive mining of knowledge at multiple levels of abstraction
Incorporation of background knowledge
Data mining query languages and ad-hoc data mining
Expression and visualization of data mining results
Handling noise and incomplete data
Pattern evaluation: the interestingness problem
Performance and scalability
Efficiency and scalability of data mining algorithms
Parallel, distributed and incremental mining methods

57. Major Issues in Data Mining (2)
Issues relating to the diversity of data types
Handling relational and complex types of data
Mining information from heterogeneous databases and global information systems (WWW)
Issues related to applications and social impacts
Application of discovered knowledge
Domain-specific data mining tools
Intelligent query answering
Process control and decision making
Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem
Protection of data security, integrity, and privacy

58. Summary
Data mining: discovering interesting patterns from large amounts of data
A natural evolution of database technology, in great demand, with wide applications
A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation
Mining can be performed in a variety of information repositories
Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.
Classification of data mining systems
Major issues in data mining

59. A Brief History of Data Mining Society
1989 IJCAI Workshop on Knowledge Discovery in Databases (Piatetsky-Shapiro)
Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)
Workshops on Knowledge Discovery in Databases
Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)
International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98)
Journal of Data Mining and Knowledge Discovery (1997)
1998 ACM SIGKDD, SIGKDD’ conferences, and SIGKDD Explorations
More conferences on data mining
PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc.

60. Where to Find References?
Data mining and KDD (SIGKDD member CDROM):
Conference proceedings: KDD, and others, such as PKDD, PAKDD, etc.
Journal: Data Mining and Knowledge Discovery
Database field (SIGMOD member CD ROM):
Conference proceedings: ACM-SIGMOD, ACM-PODS, VLDB, ICDE, EDBT, DASFAA
Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc.
AI and Machine Learning:
Conference proceedings: Machine learning, AAAI, IJCAI, etc.
Journals: Machine Learning, Artificial Intelligence, etc.
Statistics:
Conference proceedings: Joint Stat. Meeting, etc.
Journals: Annals of statistics, etc.
Visualization:
Conference proceedings: CHI, etc.
Journals: IEEE Trans. visualization and computer graphics, etc.

61. References
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of ACM, 39:58-64, 1996.
G. Piatetsky-Shapiro, U. Fayyad, and P. Smith. From data mining to knowledge discovery: An overview. In U.M. Fayyad, et al. (eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996.
G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.