Chapter I Introduction BIS 541 Spring 2006

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Chapter IIntroductionBIS 541Spring 2006
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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
• Classification of data mining systems
• Major issues in data mining

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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
• Need to convert such data into knowledge and
  information
• Applications
• Business management
• Production control
• Market analysis
• Engineering design
• Science exploration

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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

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Evolution of Database Technology (2) (See Fig.
1.1) Han

• 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.)
• 1990s2000s
• Data mining and data warehousing, multimedia
  databases, and Web databases

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Developments in computer hardware

• Powerful and affordable computers
• Data collection equipment
• Storage media
• Communication and networking

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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

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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 makers intuition
• no tool to extract knowledge embedded in vast
  amounts of data
• Expert system technology
• domain experts to input knowledge
• time consuming and costly

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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

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Data Mining vs. Data Query

• Data Querye.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 problemse.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

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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
• Classification of data mining systems
• Major issues in data mining

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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

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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.

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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. Presentation and Evaluation
• visualization, transformation, removing redundant
  patterns, etc.
• 7. Taking action
• incorporating into the performance system
• documenting
• reporting to interested parties

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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 aincomebhousesccars...
• Make neccesary transformations z scores so that
  some data mining algorithms work more efficiently

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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
• Implement 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

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Data Mining A KDD Process
Knowledge
Pattern Evaluation

• Data mining the core of knowledge discovery
  process.

Data Mining
Task-relevant Data
Selection
Data Warehouse
Data Cleaning
Data Integration
Databases
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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
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Architecture of a Typical Data Mining System

• Data base, data warehouse
• Data base or data warehouse server
• Knowledge base
• concept hierarchies
• user beliefs
• asses patterns interestingness
• other thresholds
• Data mining engine
• functional modules
• characterization, association, classification,
  cluster analysis, evolution and deviation
  analysis
• Pattern evaluation module
• Graphical user interface

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

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An Example problem

• All Electronic is a multi branch retail company
• relational tables include
• Customer
• ID,name, address, age, income,education ,sex, m
  status
• items
• ID,name,brand,category,type,price,place_made,
  supplier, cost
• employee
• ID,name,department, education, salary
• purchases
• transID, item_sold, customer ID, emp_ID, date,
  time ,method_paid,amount

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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
• Classification of data mining systems
• Major issues in data mining

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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

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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

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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

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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

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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
Model
Y output
inputs X1,X2
The user does not care what the model is doing it
is a black box interested in the accuracy of its
predictions
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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

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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

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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

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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

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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 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

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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 click stream analysis
• Similarity-based analysis
• Other pattern-directed or statistical analyses

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Concept Description

• Characterization
• Discerimination
• Data
• classes or
• concpets
• classes of items for sale
• computers, printers
• concepts of customers
• bigSpenders
• BudgetSpenders

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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

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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

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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

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Discrimination examples

• Example 1Compare 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

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Multidimensional Data

• sales according to region month and product type

Dimensions Product, Location, Time Hierarchical
summarization paths
Region
Industry Region Year Category
Country Quarter Product City Month
Week Office Day
Product
Month
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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

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Example association analysis

• From the AllEs database
• age(X,20..29)?income(X,1 billion...2
  billon)?buy(X,CD player)
• (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 CD player
• with 60 probability that customers in those age
  and income groups will buy CD player
• a multidimensional association rule
• contains more than one attribute or predicate

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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

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Example basket analysis rule

• buy(computer)?buy(printer)
• (support 1,confidence60)
• 1 of all transactions contains
• computer and printer
• if a transaction contains computer
• 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

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Classification

• Learning is supervised
• Dependent variable is categorical
• Build a model able to assign new instances to one
  of a set of well-defined classes

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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

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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

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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

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An example - classification
Historical data Each customer type Is known Each
customer has a Label

• Testing set whose labels are also
• Known but not used in model
• Training the model

• New customers Whose type hsa to be
• Estimated
• Each new customer hss to be classified as Risky
  normal or good

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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
• If accuricy of the model is satisfactory
• Use the model on the new customers
• 11 and 27 to assign a type the these new
  customers

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Example AE customers
age
goodl risky
Yearly income
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Example AE customers
age
goodl risky
?
Yearly income
Assign the new customer whose type in unknown to
either or
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Solution
good risky
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rule IF yearly incomegt 1000 and agegt 35
THEN good ELSE risky
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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

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Credit Card Promotion Database
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Decision Trees for Credit Card Insurance Database
Dependent Variable Life Insurance Promotion
lt43
gt43

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

N 3,Y 0 DecisionNo
Sex
Male
Female
A Production Rule from the Tree IF
(agelt43)(SexMale) (Credit Card In
No) THEN Life Insurance Pr No
N 0, Y 6 Decision Yes
Yes
No
Yes 2, No 0 Decision? Yes
N 4, Y 1 Decision No
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Artificial Neural Networks

• Set of interconnected nodes designed to imitate
  the functioning of the human brain
• Feed-forward network
• Supervised learner model

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Operates in two phasesFirst Phase

• First Phase learning phase
• Input values associated with each instance enter
  the network at the input layer
• Input values progress over the network
• Multiplied by weights
• Summed to form net inputs
• Pass over transfer functions at hidden layers
• Output of each instance is computed
• Compare the output with the desired network
  output
• Error difference between actual and desired
  output
• Weights are adjusted to reduce error

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Operates in two phasesSecond Phase

• Network weights are fixed
• Network is used to compute output values for new
  instances
• Predicts the output for instances whose output
  values are unknown

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Artificial Neural Nets Perseptron
x01
x1
w1
w0
x2
g
w2
y
wd
xd
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For the promotion example

• Encode all variables
• Assign a numerical value even for qualitative
  variables such as sex
• Say X1 represent sex
• When
• Male X1 1
• Female X1 0

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Input layer
Hidden layer
Output layer
1
W1,50.014
X11
5
W5,9-0.17
X20
X30.5
X4-1
(1-0.78)2 is error square 1 actual value of O9
for a particular Data object 0.78 is predicted
value
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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

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Estimation-Prediction

• Similar to classification
• Output is a continuous variable
• Estimation current value
• Prediction future outcome rather then current
  behavior

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Typical Estimation-Prediction Problems

• Estimate the salary of an individual who owns a
  sports car
• Predict next weeks closing price for the IMKB100
  index
• Forecast next days temperature

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Prediction methods

• Artificial Neural networks
• linear regression
• Yi a0a1X1,ia2X2,i...akXk,iui
• 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

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ExamplePrediction 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

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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

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Example Clustering

• Can be performed on AE customer data
• to identify homogenous subpopulations of
  customers
• represent individual target groups for marketing

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distance
Type1
Type 2
type 3
income
Clustering according to income and distance to
store three cluster of data points are evident
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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

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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

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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

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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

75
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
• Classification of data mining systems
• Major issues in data mining

76
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

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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 users 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 patternsmining
  query optimization

79
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
• Classification of data mining systems
• Major issues in data mining

80
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

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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,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.

82
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

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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

84
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 fraudASPECT 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.

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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

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Financial Data Analysis

• Financial data
• complete, reliable, high quality
• Loan payment prediction and customer credit
  policy analysis

87
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

88
Risk Management and Insurance

• determine insurance rates
• manage investment portfolios
• differentiate between companies and/or
  individuals who are good and poor credit risks
• Farmers 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

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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

90
Other Applications

• Sports and Gaming
• 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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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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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, 3958-64, 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.