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Intelligence Through Learning from Data

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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.

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