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Intelligence Through Learning from Data

Monash University

Semester 1, March 2006

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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

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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

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Machine Learning

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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.

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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

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The Empirical Cycle

Analysis

Observation Theory

Prediction

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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.

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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.

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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

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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.

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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.

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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

AlgorithmFile-inFile-out

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Data Mining

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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

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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

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Evolution of Database Technology

• 1960s:

– 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

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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

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Episodes Database GP Database

Rules 1% supportIf test A then test B will occur in 62%

of cases

Segment 1 Segment 2 97 GPs 206 GPsScore = 1.8 Score = 2.7

Data Preparation Merge

Association Discovery Database Segmentation

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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, email, documents) and Web analysis.

– Intelligent query answering

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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

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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)

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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

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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

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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.

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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.

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Data Mining: A KDD Process

– Data mining: the core of knowledge discovery process.

Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

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The Process of Knowledge Discovery

Data Cleaning & Enrichment

Coding Data mining Reporting

selection-domain consistency

- clustering- segmentation

-de-duplication - prediction

-disambiguation

InformationRequirement

Action

Feedback

Operational data External data

The Knowledge Discovery in Databases (KDD) process (Adriens/Zantinge)

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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

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Data Mining and Business Intelligence

Increasing potentialto supportbusiness decisions End User

Business Analyst

DataAnalyst

DBA

MakingDecisions

Data Presentation

Visualization TechniquesData Mining

Information Discovery

Data Exploration

OLAP, MDA

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

Data Sources

Paper, Files, Information Providers, Database Systems, OLTP

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Architecture of a Typical Data Mining System

Data Warehouse

Data cleaning & data integration Filtering

Databases

Database or data warehouse server

Data mining engine

Pattern evaluation

Graphical user interface

Knowledge-base

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Association Rule Mining

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.

ConfidenceRule Body

Rule Head Support

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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

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Co-Occurrence Table

Customer Items1 orange juice (OJ), cola2 milk, orange juice, window cleaner3 orange juice, detergent4 orange juice, detergent, cola5 window cleaner, cola

OJ Cleaner Milk Cola DetergentOJ 4 1 1 2 2Cleaner 1 2 1 1 0Milk 1 1 1 0 0Cola 2 1 0 3 1Detergent 2 0 0 1 2

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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)

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Support and Confidence

• Support:– 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)

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What is a Rule?

If condition then resultNote:

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 CIf A and C, then B

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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

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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

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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.

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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

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Data Mining: Confluence of Multiple Disciplines

Data Mining

Database Technology

Statistics

OtherDisciplines

InformationScience

MachineLearning Visualization

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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

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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.

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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

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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

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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

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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

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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

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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)

• 1991-1994 Workshops on Knowledge Discovery in Databases– Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-

Shapiro, P. Smyth, and R. Uthurusamy, 1996)

• 1995-1998 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’1999-2001 conferences, and SIGKDD Explorations

• More conferences on data mining– PAKDD, PKDD, SIAM-Data Mining, (IEEE) ICDM, etc.

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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.

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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, 1-35. AAAI/MIT Press, 1996.

• G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases.

AAAI/MIT Press, 1991.