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Chapter I:Introduct ion BIS 541 20 13/2014 Summer. Chapter 1. Introduction. Motivation: Why data mining? Methodology of Knowledge Discovery in Databases Data mining functionalities Are all the patterns interesting? Business a pplications of data mining.

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chapter 1 introduction
Chapter 1. Introduction
  • Motivation: Why data mining?
  • Methodology of Knowledge Discovery in Databases
  • Data mining functionalities
  • Are all the patterns interesting?
  • Business applications of data mining
motivation necessity is the mother of invention
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
  • Need to convert such data into knowledge and information
  • Applications
    • Business management
    • Production control
    • Market analysis
    • Engineering design
    • Science exploration
evolution of database technology 1
Evolution of Database Technology (1)
  • Data collection, database creation
  • Data management
    • data storage and retrieval
    • database transaction processing
  • Data analysis and understanding
    • Data mining and data warehousing
evolution of database technology 2
Evolution of Database Technology (2)
  • 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.)
    • Application-oriented DBMS (spatial, scientific, engineering, etc.)
  • 1990s:
    • Data mining, data warehousing, multimedia databases, and Web databases
  • 2000s
    • Stream data management and mining
    • Data mining and its applications
    • Web technology (XML, data integration) and global information systems
The Explosive Growth of Data: from terabytes to petabytes
    • Data collection and data availability
      • Automated data collection tools, database systems, Web, computerized society
    • Major sources of abundant data
      • Business: Web, e-commerce, transactions, stocks, …
      • Science: Remote sensing, bioinformatics, scientific simulation, …
      • Society and everyone: news, digital cameras, YouTube
  • We are drowning in data, but starving for knowledge!
  • “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
developments in computer hardware
Developments in computer hardware
  • Powerful and affordable computers
  • Data collection equipment
  • Storage media
  • Communication and networking
data warehouse
Data Warehouse
  • Data cleaning
  • Data integration
  • OLAP: On-Line Analytical Processing
    • summarization
    • consolidation
    • aggregation
    • view information from different angles
  • but additional data analysis tools are needed for
    • classification
    • clustering
    • charecterization of data changing over time
data rich information poor situation
Data rich information poor situation
  • Abundance of data
  • need for powerful data analysis tools
  • “data tombs” - data archives
    • seldom visited
  • Important decisions are made
    • not on the information rich data stored in databases
    • but on a decision maker’s intuition
  • no tool to extract knowledge embedded in vast amounts of data
  • Expert system technology
    • domain experts to input knowledge
    • time consuming and costly
what is data mining
What Is Data Mining?
  • Data mining (knowledge discovery in databases):
    • 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?
    • query processing.
    • Expert systems or small ML/statistical programs
data mining vs data query
Data Mining vs. Data Query
  • Data Query:e.g.
    • A list of all customers who use a credit card to buy a PC
    • A list of all MIS students having a GPA of 3.5 or higher and has studied 4 or less semesters
  • Data Mining problems:e.g.
    • What is the likelihood of a customer purchasing PC with credit card
    • Given the characteristics of MIS students predict her SPA in the comming term
    • What are the characteristics of MIS undergrad students

Chapter 1. Introduction

  • Motivation: Why data mining?
  • Methodology of Knowledge Discovery in Databases
  • Data mining functionalities
  • Are all the patterns interesting?
  • Business applications of data mining
why data mining
Why Data Mining?
  • Four questions to be answered
  • Can the problem clearly be defined?
  • Does potentially meaningful data exists?
  • Does the data contain hidden knowledge or useful only for reporting purposes?
  • Will the cost of processing the data will be less then the likely increase in profit from the knowledge gained from applying any data mining project
steps of a kdd process 1
Steps of a KDD Process(1)
  • 1. Goal identification:
    • Define problem
    • relevant prior knowledge and goals of application
  • 2. Creating a target data set: data selection
  • 3. Data preprocessing: (may take 60%-80% of effort!)
    • removal of noise or outliers
    • strategies for handling missing data fields
    • accounting for time sequence information
  • 4. Data reduction and transformation:
    • Find useful features, dimensionality/variable reduction, invariant representation.
steps of a kdd process 2
Steps of a KDD Process(2)
  • 5. Data Mining:
    • Choosing functions of data mining:
      • summarization, classification, regression, association, clustering.
    • Choosing the mining algorithm(s):
      • which models or parameters
    • Search for patterns of interest
  • 6. Presentationand Evaluation:
    • visualization, transformation, removing redundant patterns, etc.
  • 7. Taking action:
    • incorporating into the performance system
    • documenting
    • reporting to interested parties
an example c ustomer s egmentation
An example: Customer Segmentation
  • 1. Marketing department wants to perform a segmentation study on the customers of AE Company
  • 2. Decide on revevant variables from a data warehouse on customers, sales, promotions
    • Customers: name,ID,income,age,education,...
    • Sales: hisory of sales
    • Promotion: promotion types durations...
  • 3. Hendle missing income, addresses..
  • determine outliers if any
  • 4. Cenerate new index variables representing wealth of customers
    • Wealth = a*income+b*#houses+c*#cars...
    • Make neccesary transformations z scores so that some data mining algorithms work more efficiently
e xample c ustomer s egmentation cont
Example: Customer Segmentation cont.
  • 5.a: Choose clustering as the data mining functionality as it is the natural one for a segmentation study so as to find group of customers with similar charecteristics
  • 5.b: Choose a clustering algorithm
    • K-means or k-medoids or any suitable one for that problem
  • 5.c: Apply the algorithm
    • Find clusters or segments
  • 6. make reverse transformations, visualize the customer segments
  • 7. present the results in the form of a report to the marketing deprtment
    • İmplement the segmentation as part of a DSS so that it can be applied repeatedly at certain internvals as new customers arrive
    • Develop marketing strategies for each segment
data mining a kdd process
Data Mining: A KDD Process


Pattern Evaluation

  • Data mining: the core of knowledge discovery process.

Data Mining

Task-relevant Data


Data Warehouse

Data Cleaning

Data Integration


data mining in business intelligence
Data Mining in Business Intelligence

Increasing potential

to support

business decisions

End User




Data Presentation

Visualization Techniques

Data Mining



Information Discovery

Data Exploration

Statistical Summary, Querying, and Reporting

Data Preprocessing/Integration, Data Warehouses


Data Sources

Paper, Files, Web documents, Scientific experiments, Database Systems

September 3, 2014


Data Mining: Concepts and Techniques

architecture of a typical data mining system
Architecture of a Typical Data Mining System

Graphical user interface

Pattern evaluation

Data mining engine


Database or data warehouse server


Data cleaning & data integration




architecture of a typical data mining system1
Architecture of a Typical Data Mining System
  • Data base, data warehouse
  • Data base or data warehouse server
  • Knowledge base
    • concept hierarchies
    • user beliefs
      • asses pattern’s interestingness
    • other thresholds
  • Data mining engine
    • functional modules
      • characterization, association, classification, cluster analysis, evolution and deviation analysis
  • Pattern evaluation module
  • Graphical user interface

Data Mining: Confluence of Multiple Disciplines




Data Mining








why confluence of multiple disciplines
Why Confluence of Multiple Disciplines?

Tremendous amount of data

Algorithms must be highly scalable to handle such as tera-bytes of data

High-dimensionality of data

Micro-array may have tens of thousands of dimensions

High complexity of data

Data streams and sensor data

Time-series data, temporal data, sequence data

Structure data, graphs, social networks and multi-linked data

Heterogeneous databases and legacy databases

Spatial, spatiotemporal, multimedia, text and Web data

Software programs, scientific simulations

New and sophisticated applications

September 3, 2014


Data Mining: Concepts and Techniques

efficient and scalable techniques
Efficient and Scalable Techniques
  • For an algorithm to be efficient and scalable
  • its running time should be predictable and acceptable
  • How
    • Parallel and distributed algorithms
    • Sampling from databases

Chapter 1. Introduction

  • Motivation: Why data mining?
  • Methodology of Knowledge Discovery in Databases
  • Data mining functionalities
  • Are all the patterns interesting?
  • Business applications of data mining
two styles of data mining
Two Styles of Data Mining
  • Descriptive data mining
    • characterize the general properties of the data in the database
    • finds patterns in data and
    • the user determines which ones are important
  • Predictive data mining
    • perform inference on the current data to make predictions
    • we know what to predict
  • Not mutually exclusive
    • used together
    • Descriptive  predictive
  • Eg. Customer segmentation – descriptive by clustering
  • Followed by a risk assignment model – predictive by ANN

Supervised vs. Unsupervised Learning

  • Supervised learning (classification, prediction)
    • Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
    • New data is classified based on the training set
  • Unsupervised learning(summarization. association, clustering)
    • The class labels of training data is unknown
    • Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
descriptive data mining 1
Descriptive Data Mining (1)
  • Discovering new patterns inside the data
  • Used during the data exploration steps
  • Typical questions answered by descriptive data mining
    • what is in the data
    • what does it look like
    • are there any unusual patterns
    • what dose the data suggest for customer segmentation
  • users may have no idea
    • which kind of patterns may be interesting
descriptive data mining 2
Descriptive Data Mining (2)
  • patterns at verious granularities
    • geograph
      • country - city - region - street
    • student
      • university - faculty - department - minor
  • Fuctionalities of descriptive data mining
    • Clustering
      • Ex: customer segmentation
    • summarization
    • visualization
    • Association
      • Ex: market basket analysis

A model is a black box

X: vector of independent variables or inputs

Y =f(X) : an unknown function

Y: dependent variables or output

a single variable or a vector


Y output



The user does not care what the model is doing

it is a black box

interested in the accuracy of its predictions

predictive data mining 1
Predictive Data Mining (1)
  • Using known examples the model is trained
    • the unknown function is learned from data
  • the more data with known outcomes is available
    • the better the predictive power of the model
  • Used to predict outcomes whose inputs are known but the output values are not realized yet
  • Never %100 accurate
predictive data mining 2
Predictive Data Mining (2)
  • The performance of a model on past data is not important
    • to predict the known outcomes
  • Its performance on unknown data is much more important
typical questions answered by predictive models
Typical questions answered by predictive models
  • Who is likely to respond to our next offer
    • based on history of previous marketing campaigns
  • Which customers are likely to leave in the next six months
  • What transactions are likely to be fraudulent
    • based on known examples of fraud
  • What is the total amount spending of a customer in the next month
data mining functionalities 1
Data Mining Functionalities (1)
  • Concept description: Characterization and discrimination
    • Generalize, summarize, and contrast data characteristics, e.g., big spenders vs. budget spenders
  • Association (correlation and causality)
    • Multi-dimensional vs. single-dimensional association
    • age(X, “20..29”) ^ income(X, “20..29K”) à buys(X, “PC”) [support = 2%, confidence = 60%]
    • contains(T, “computer”) à contains(x, “software”) [1%, 75%]
data mining functionalities 2
Data Mining Functionalities (2)
  • Classification and Prediction
    • Finding models (functions) that describe and distinguish classes or concepts for future prediction
    • E.g., classify people as healty or sick, or classify transactions as fraudulent or not
    • Methods: 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 customers of a retail company to learn about characteristics of different segments
    • Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity
data mining functionalities 3
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: click stream analysis
    • Similarity-based analysis
  • Other pattern-directed or statistical analyses
concept description
Concept Description
  • Characterization
  • Discerimination
  • Data
    • classes or
    • concpets
  • classes of items for sale
    • computers, printers
  • concepts of customers:
    • bigSpenders
    • BudgetSpenders
data characterization
Data Characterization
  • Summarization the data of the class under study (target class)
  • Methods
    • SQL queries
    • OLAP roll up -operation
      • user-controlled data summarization
      • along a specified dimension
    • attribute oriented induction
      • without step by step user interraction
  • the output of characterization
    • pie charts, bar chars, curves, multidimensional data cube, or cross tabs
    • in rule form as characteristic rules
characterization example
Characterization example
  • Description summarizing the characteristics of customers who spend more than $1000 a year at AllElecronics
    • age, employment, income
    • drill down on any dimension
      • on occupation view these according to their type of employment
data discrimination
Data Discrimination
  • Comparing the target class with one or a set of comparative classes (contrasting classes)
    • these classes can be specified by the use
  • database queries
  • methods and output
    • similar to those used for characterization
    • include comparative measures to distinguish between the target and contrasting classes
discrimination examples
Discrimination examples
  • Example 1:Compare the general features of software products
    • whose sales increased by %10 in the last year (target class)
    • whose sales decreased by at least %30 during the same period (contrasting class)
  • Example 2: Compare two groups of AE customers
    • I) who shop for computer products regularly (target class)
      • more than two times a month
    • II) who rarely shop for such products (contrasting class)
      • less than three times a year
  • The resulting description:
  • %80 of I group customers
    • university education
    • ages 20-40
  • %60 of II group customers
    • seniors or young
    • no university degree

Multidimensional Data

  • sales according to region month and product type

Dimensions: Product, Location, Time

Hierarchical summarization paths


Industry Region Year

Category Country Quarter

Product City Month Week

Office Day




Association Analysis

  • Discovery of association rules showing attribute-value conditions that occur frequently together in a given set of data
  • widely used
    • market basket
    • transaction data analysis
  • more formally
  • X  Y that is
  • A1A2.. Ak B1B2.. Bl
  • A1 , B1 are attribute value pairs or predicates

Example: association analysis

  • From the AllEs database
    • age(X,”20..29”)income(X,”1,000...2,000”)buy(X,”Notebook computer”)
    • (support = %2,
    • confidence= %60)
  • X is a variable representing a customer
  • %2 of the AE customers are
    • between 20 and 29 age
    • incomes ranging from 1 to 2 billon TL
    • Buy Notebook
  • with %60 probability that customers in those age and income groups will buynote book
  • a multidimensional association rule
    • contains more than one attribute or predicate
market basket analysis
Market basket analysis
  • customers buying behaviour is investigated
  • Based on only the transactions data
    • no information about customer properties: age income
  • Managers
    • are interested in which products or product groups are sold together
example basket analysis rule
Example: basket analysis rule
    • buy(notebok)buy(printer)
    • (support= %1,confidence=%60)
    • %1 of all transactions contains
      • computer and printer
    • if a transaction containsnotebook
      • there is a %60 chance that it contains printer as well
  • a single dimensional association rule
    • contains a single predicate
  • an association rule is interesting if
    • its support exceeds a minimum threshold and
    • its confidence exceeds a min threshold
  • These min values are set by specialists
  • Learning is supervised
  • Dependent variable is categorical
  • Build a model able to assign new instances to one of a set of well-defined classes
typical classification problems
Typical Classification Problems
  • Given characteristics of individuals differentiate them who have suffered a heart attack from those who have not
  • Determine if a credit card purchase is fraudulent
  • Classify a car loan applicant as a good or a poor credit risk
methods of classification
Methods of Classification
  • Decision Trees
  • Artificial Neural Networks
  • Bayesian Classification
    • Naïve
    • Belief Networks
  • k-nearest neighbor
  • Regression
    • Logistic (logit) probit
      • Predicts probability of each class
      • when the dependent variable is categorical
        • good customer bed customer or employed unemployed
steps of classification process
Steps of classification process
  • (1) Train the model
    • using a training set
    • data objects whose class labels are known
  • (2) Test the model
    • on a test sample
    • whose class labels are known but not used for training the model
  • (3) Use the model for classification
    • on new data whose class labels are unknown
an example classification
An example - classification

Historical dataEach customer type İs known

Each customer has aLabel

  • Testing set whose labels are also
  • Known but not used in model
  • Training the model
  • New customersWhose type hsa to be
  • Estimated
  • Each new customer hss to be classified as Risky normal or good

Historical dataEach customer type İs known

Each customer has aLabel

  • Testing set whose labels are also
  • Known but not used in model
  • Training the model
  • New customersWhose type hsa to be
  • Estimated
  • Each new customer hss to be classified as buyer or non buyer
an example classification cont
An example – classification cont.
  • Based on historical data develop a classification model
    • Decision tree, neural network, regression ...
  • Test the performance of the model on a portion of the historical data
  • İf accuricy of the model is satisfactory
  • Use the model on the new customers
    • 11 and 27 to assign a type the these new customers

Example AE customers




Yearly income


Example AE customers





Yearly income

Assign the new customer whose type in unknown to

either * or +


x2 : age

x1 : yearly income






rule: IF yearly income> 1000and age> 35

THEN good ELSE risky

credit card promotion policy
Credit Card Promotion Policy
  • Credit card companies
    • Promotional offerings with their monthly credit card billing
    • Offers provide the opportunity to purchase items such as magazines, …
  • A data mining study
    • Predict individual behaviour
    • What is the likelihood of an individual towards taking the advantage of promotions
    • based on individual characteristics, credit history..
    • Expected reduction in postage; paper and processing costs for the credit card company


Cr Ins

Decision Trees for Credit Card Insurance Database

Dependent Variable

Life Insurance Promotion



  • critical value of 43
  • is deter by the
  • algorithm

N 3,Y 0





A Production Rule

from the Tree

IF (age<=43)&(Sex=Male)

&(Credit Card In = No)

THEN Life Insurance Pr = No

N 0, Y 6

Decision: Yes



Yes 2, No 0

Decision? Yes

N 4, Y 1

Decision: No

artificial neural networks
Artificial Neural Networks
  • Set of interconnected nodes designed to imitate the functioning of the human brain
  • Feed-forward network
    • Supervised learner model
for the promotion example
For the promotion example
  • Encode all variables
  • Assign a numerical value even for qualitative variables such as sex
  • Say X1 represent gender
  • When
    • Male X1 =1
    • Female X1 =0















(1-0.78)2 is error square

1 actual value of O9 for a particular

Data object 0.78 is predicted value

weights updating
Weights updating
  • Weights between nodes are adjusted so as to reduce error
  • Details of the training process for neural networks are not important for the time being
estimation prediction
  • Similar to classification
  • Output is a continuous variable
  • Estimation: current value
  • Prediction: future outcome rather then current behavior
typical estimation prediction problems
Typical Estimation-Prediction Problems
  • Estimate the salary of an individual who owns a sports car
  • Predict next week`s closing price for the IMKB100 index
  • Forecast next days temperature

Prediction methods

  • Artificial Neural networks
  • linear regression
    • Yi = a0+a1X1,i+a2X2,i+...+akXk,i+ui
  • non-linear regression
    • Yi =f(X1,i, X2,i,.., Xk,ia1,a2,..,ak,ui)
  • generalized linear regression
    • logistic
      • logit,probit
    • poisson regression
      • for count variables
  • Regression Trees
example prediction and classification
Example:Prediction and Classification
  • Classification is used to classify customers applying for credit cards
    • known class labels: risky,reliable
    • when a new customer applies looking at her charecteristics
      • income age education wealth region ...
    • Customer class is predicted
  • Prediction: The monthly expense of a new customer ( a real continuous variable ) is predicted based on personal information
    • independent variables
      • income education wealth profession ...
      • Some are numeric some categorical
cluster analysis
Cluster Analysis
  • Class label is unknown: Group data to form new classes,
  • assign class labels to each data object
    • Unknown generated by the clustering model
  • e.g., cluster customers to find customer segments
  • Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity
    • Objects within a cluster have high similarity in comparison to one another
    • but are very dissimilar to objects in other clusters
  • there may be hierarchy of classes
example clustering
Example: Clustering
  • Can be performed on AE customer data
  • to identify homogenous subpopulations of customers
  • represent individual target groups for marketing

Before clustering

After clustering




Type 2

type 3


Clustering according to income and distance to store

three cluster of data points are evident

outlier analysis
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
  • DECTECED using
    • statistical tests
    • distance measures
    • visually inspecting the data
  • Examples:
reasons for outliers
Reasons for outliers
  • Measurement errors
  • coding errors
    • age is entered as 999
  • nature of data
    • salary of the general manager is much more higher than the other employees
    • in crisis the interest rate was in the order of 1000s
evolution analysis
Evolution Analysis
  • Describes and models regularities or trends for objects whose behavior changes over time
  • Distinct features include
    • Trend and deviation: time-series data analysis
    • Sequential pattern mining, periodicity analysis
    • Similarity-based analysis
  • Example
    • Stock market predictions: future stock prices
    • for overall stocks: indexes or individual company stocks
sequential pattern analysis
Sequential Pattern Analysis
  • Determine sequential patterns in data
  • Based on time sequence of actions
  • Similar to associations
    • Relationship is based on time
  • Example 1: buy CD player today buy CD within one week
  • Example 2: In what sequence web pages of an e-business company are accessed
  • %70 percents of visitors follows
    • A B C or A D B C or A E B C
    • He then determines to add a link directly from page A to page C

Chapter 1. Introduction

  • Motivation: Why data mining?
  • Methodology of Knowledge Discovery in Databases
  • Data mining functionalities
  • Are all the patterns interesting?
  • Business applications of data mining
are all the discovered patterns interesting
Are All the “Discovered” Patterns Interesting?
  • A data mining system/query may generate thousands of patterns, not all of them are interesting.
  • Are all patterns interesting?
    • Typically not -only a small fraction of patterns are interesting to any given user
  • 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 vs. subjective interestingness measures:
  • Objective:
    • Objective: based on statistics and structures of patterns, e.g.,
      • support,
      • X Y P(X  Y):probability of a transaction contains both X and Y
    • confidence, degree of certainty of the detected association
    • P(Y I X) the conditional probability : the probability that a transaction containing X also contains Y
    • thresholds - controlled by the user
    • ex: rules that do not satisfy a confidence threshold of %50 are uninteresting
  • Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.

Chapter 1. Introduction

  • Motivation: Why data mining?
  • Methodology of Knowledge Discovery in Databases
  • Data mining functionalities
  • Are all the patterns interesting?
  • Business Applications of data mining
potential business applications
Potential Business Applications
  • Market analysis and management
      • target marketing, customer relation management, market basket analysis, cross selling, market segmentation
  • Risk analysis and management
    • Banks assume a financial risk when they grant loans
      • risk models attempt to predict the probability of default or fail to pay back the borrowed amount
      • Credit cards
    • Insurance companies
  • Fraud detection and management
  • Other Applications
    • Text mining (news group, email, documents) and Web analysis.
    • Intelligent query answering
market analysis and management 1
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,clickstreams
  • Customer profiling-segmentation
    • data mining can tell you what types of customers buy what products (clustering or classification)
  • Target marketing
    • Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.
market analysis and management 2
Market Analysis and Management (2)
  • Effectiveness of sales campaigns
    • Advertisements, coupons, discounts, bonuses
    • promote products and attract customers
    • can help improve profits
    • Compare amount of sales and number of transactions
      • during the sales period versus before or after the sales campaign
    • Association analysis
      • which items are likely to be purchased together with the items on sale
market analysis and management 3
Market Analysis and Management (3)
  • Customer retention Analysis of Customer loyalty
    • sequences of purchases of particular customers
    • goods purchased at different periods by the same customers can be grouped into sequences
    • changes in customer consumption or loyalty
    • suggests adjustments on the pricing and variety of goods
    • to retain old customers and attract new customers
  • Cross-selling and up-selling
    • associations from sales records
    • a customer who buy a PC is likely to buy a printer
    • purchase recommendations
fraud detection and management
Fraud Detection and Management
  • 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
    • Credit card transactions: The FALCON fraud assessment system by HNC Inc. to signal possibly fraudulent credit card transactions
    • money laundering: detect suspicious money transactions (US Treasury\'s Financial Crimes Enforcement Network)
    • Detecting telephone fraud:ASPECT European Research Gr.
      • Unsupervised clustering to detect fraud in mobile phone networks
      • Telephone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm.
health care
Health Care
  • Storing patients` records in electronic format, developments in medical information systems
    • Large amount of clinical data
  • Regularities, trends and surprising events extracted by data mining methods
    • ANN, temporal reasoning
    • assist clinicians to make informed decisions and improving health sevices
  • MERCK-MEDCO Managed Care, Pharmaceutical Insurance … company
    • Uncover less expensive but equally effective drug treatments
financial data analysis
Financial Data Analysis
  • Financial data
    • complete, reliable, high quality
  • Loan payment prediction and customer credit policy analysis
loan payment prediction and customer credit policy analysis
Loan payment prediction and customer credit policy analysis
  • Factors influencing loan payment performance
    • loan-to-value ratio
    • term of the loan
    • dept ratio (total monthly debt/total monthly income)
    • payment-to-income ratio
    • income level
    • education level
    • residence region
    • credit history
  • analysis may find that
    • payment-income ratio is a dominant factor while
    • education level and debt ratio are not
risk management and insurance
Risk Management and Insurance
  • determine insurance rates
  • manage investment portfolios
  • differentiate between companies and/or individuals who are good and poor credit risks
  • Farmer`s Group discover a scenario:
    • Someone who owns a sports car is not a higher accident risk
    • Conditions: the sport car to be a second car and the family car to be a station wagon or a sedan
data mining for the telecommunication industry
Data Mining for the Telecommunication Industry
  • Telecommunication data are multidimensional
    • calling-time duration
    • location of caller location of callee
    • type of call
  • used to identify and compare
    • data traffic system workload
    • resource usage user group behavior
    • profit
  • fraudulent pattern analysis and identification of unusual patterns
  • to achieve customer loyalty
  • characteristics of customers affecting line usage

Other Applications

  • Sports and Gaming
    • Predicting outcome of football games
  • Text Mining
    • Spam detection
  • Internet Web Mining
    • Web usage mining
      • İmprove link structure
      • Recommander Systmes
    • Web structure mining: mining link structure of Web

Other Applications

  • Educational Data Mining
    • Clustering students
    • Design enterece exams, selection policies
  • Human Resources
    • How to select applicants
  • Online Dating
    • Recommandataions to visitors
  • 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