business intelligence data mining techniques as tools for business intelligence
TRANSCRIPT
Business IntelligenceBusiness Intelligence
Data Mining Techniques As Tools for Data Mining Techniques As Tools for
Business IntelligenceBusiness Intelligence
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IntroductionIntroduction
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
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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.
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Why Data Mining? — Why Data Mining? — Potential ApplicationsPotential Applications
Database analysis and decision support Market analysis and management
target marketing, customer relation management, market basket analysis, cross selling, market segmentation
Risk analysis and management Forecasting, customer retention, improved
underwriting, quality control, competitive analysis Fraud detection and management
Other Applications Text mining (news group, email, documents) and
Web analysis. Intelligent query answering
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Market Analysis and Management Market Analysis and Management (1)(1)
Where are the data sources for analysis? Credit card transactions, loyalty cards, discount coupons,
customer complaint calls, plus (public) lifestyle studies
Target marketing Find clusters of “model” customers who share the same
characteristics: interest, income level, spending habits, etc.
Determine customer purchasing patterns over time Conversion of single to a joint bank account: marriage, etc.
Cross-market analysis Associations/co-relations between product sales Prediction based on the association information
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Market Analysis and Management Market Analysis and Management (2)(2)
Customer profiling data mining can tell you what types of customers
buy what products (clustering or classification) Identifying customer requirements
identifying the best products for different customers
use prediction to find what factors will attract new customers
Provides summary information various multidimensional summary reports statistical summary information (data central
tendency and variation)
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Corporate Analysis and Risk Corporate Analysis and Risk ManagementManagement Finance planning and asset evaluation
cash flow analysis and prediction contingent claim analysis to evaluate assets cross-sectional and time series analysis (financial-
ratio, trend analysis, etc.) Resource planning:
summarize and compare the resources and spending
Competition: monitor competitors and market directions group customers into classes and a class-based
pricing procedure set pricing strategy in a highly competitive market
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Fraud Detection and Management Fraud Detection and Management (1)(1) Applications
widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc.
Approach use historical data to build models of fraudulent behavior
and use data mining to help identify similar instances Examples
auto insurance: detect a group of people who stage accidents to collect on insurance
money laundering: detect suspicious money transactions (US Treasury's Financial Crimes Enforcement Network)
medical insurance: detect professional patients and ring of doctors and ring of references
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Fraud Detection and Management Fraud Detection and Management (2)(2) Detecting inappropriate medical treatment
Australian Health Insurance Commission identifies that in many cases blanket screening tests were requested (save Australian $1m/yr).
Detecting telephone fraud Telephone call model: destination of the call,
duration, time of day or week. Analyze patterns that deviate from an expected norm.
British Telecom identified discrete groups of callers with frequent intra-group calls, especially mobile phones, and broke a multimillion dollar fraud.
Retail Analysts estimate that 38% of retail shrink is due to
dishonest employees.
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Data mining: the core of knowledge discovery process.
Data Mining: A KDD ProcessData Mining: A KDD Process
DB-03
DB-01DB-01
DATA SOURCES
DATAWAREHOUSEData
Pre-Processing
DataSelection
Data Integration
Task RelevantData
DataMining
100%
90%
80%
70%
60%
50%
40%
30%
40%
50%
DM Models
ModelEvaluation
KNOWLEDGE
Feedback: Knowledge Integration
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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.
Deployement: Use of discovered knowledge
Steps of a KDD ProcessSteps of a KDD Process
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Standardized Data Mining Standardized Data Mining ProcessesProcesses
Step 1: Business Understanding
Determine the business objectives
Assess the situation Determine the data
mining goals Produce a project plan
Cross-Industry Standard Process for Data Mining CRISP-DM
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Standardized Data Mining Standardized Data Mining ProcessesProcesses
Step 2: Data Understanding
Collect the initial data
Describe the data Explore the data Verify the data
Cross-Industry Standard Process for Data Mining CRISP-DM
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Standardized Data Mining Standardized Data Mining ProcessesProcesses
Step 3: Data Preparation Select data Clean data Construct data Integrate data Format data
Cross-Industry Standard Process for Data Mining CRISP-DM
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Standardized Data Mining Standardized Data Mining ProcessesProcesses
Step 4: Modeling Select the modeling
technique Generate test
design Build the model Assess the model
Cross-Industry Standard Process for Data Mining CRISP-DM
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Standardized Data Mining Standardized Data Mining ProcessesProcesses
Step 5: Evaluation Evaluate results Review process Determine next step
Cross-Industry Standard Process for Data Mining CRISP-DM
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Standardized Data Mining Standardized Data Mining ProcessesProcesses
Step 6: Deployment Plan deployment Plan monitoring and
maintenance Produce final report Review the project
Cross-Industry Standard Process for Data Mining CRISP-DM
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Architecture of a Architecture of a Typical Data Mining SystemTypical Data Mining System
USER
INTERFACE
Best Data Mining Tool
DATA
WAREHOUSE
Statistical Components:
. Data Cleaning
. Data Transformation
. Exploratory Analysis
. Factor Analysis
. ...
Data Mining Components:
. Decision Trees
. Association Rules
. Clustering
. Visualization
. ...
Output
Input
User
DataSources
DomainKnowledge
Base
Data . Cleaning . Integration . Transformatin
Data . Cleaning . Integration . Transformatin
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Data Mining Functionalities (1)Data Mining Functionalities (1)
Concept description: Characterization and discrimination
Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions
Association (correlation and causality) Multi-dimensional vs. single-dimensional association age(X, “20..29”) ^ income(X, “20..29K”) buys(X,
“PC”) [support = 2%, confidence = 60%] contains(T, “computer”) contains(x, “software”)
[1%, 75%]
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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, ANN 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
Data Mining Functionalities (2)Data Mining Functionalities (2)
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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, periodicity analysis Similarity-based analysis
Other pattern-directed or statistical analyses
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Data Mining: Data Mining: Combination of Multiple DisciplinesCombination of Multiple Disciplines
STATISTICS
MACHINELEARNING
DATABASETECHNOLOGY
VISUALIMAGING
OTHERDISCIPLINES
INFORMATIONSCIENCE
DATAMINING
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A Multi-Dimensional View of Data A Multi-Dimensional View of Data Mining ClassificationMining 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 extracted Characterization, discrimination, association, classification,
clustering, trend, deviation and outlier analysis, etc. Multiple/integrated functions and mining at multiple levels
Techniques to utilized Database-oriented, data warehouse (OLAP), machine
learning, statistics, visualization, neural network, etc. Applications adapted
Retail, telecommunication, banking, fraud analysis, DNA mining, stock market analysis, Web mining, Weblog analysis, etc.
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Poll: Which data mining Poll: Which data mining technique..?technique..?
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1. Association1. Association
Market Basket Analysis
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Association RulesAssociation Rules
A.K.A. Association rule mining Mining association rules from transactional
databases using Apriory algorithm Other Methods…
Mining multilevel association rules from transactional databases
Mining multidimensional association rules from transactional databases and data warehouse
From association mining to correlation analysis Constraint-based association mining
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What Is Association Mining?What Is Association Mining?
Association rule mining: Finding frequent patterns, associations,
correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories.
Applications: Market basket analysis, cross-marketing, catalog
design, loss-leader analysis, clustering, classification, etc.
Examples: Rule form: “Body Head [support, confidence]”
buys(x, “diapers”) buys(x, “beers”) [0.5%, 60%] major(x, “CS”) ^ takes(x, “DB”) grade(x, “A”) [1%,
75%]
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2. What is Cluster Analysis?2. What is Cluster Analysis?
Cluster: a collection of data objects… Similar to one another within the same
cluster Dissimilar to the objects in other clusters
Cluster analysis Grouping a set of data objects into clusters
Clustering is unsupervised classification: no predefined classes
Typical applications As a stand-alone tool to get insight into data
distribution As a preprocessing step for other algorithms
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General Applications of ClusteringGeneral Applications of Clustering
Pattern Recognition Spatial Data Analysis Image Processing Economic Science WWW
Document classification Cluster Weblog data to discover
groups of similar access patterns
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Examples of Clustering ApplicationsExamples of Clustering Applications
Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs
Land use: Identification of areas of similar land use in an earth observation database
Insurance: Identifying groups of motor insurance policy holders with a high average claim cost
City-planning: Identifying groups of houses according to their house type, value, and geographical location
Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults
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The K-Means Clustering MethodThe K-Means Clustering Method
Example
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Decision tree… A flow-chart-like tree structure Internal node denotes a test on an attribute Branch represents an outcome of the test Leaf nodes represent class labels or class distribution
Decision tree generation consists of two phases Tree construction
At start, all the training examples are at the root Examples are recursively partitioned based on selected
attributes Tree pruning
Identify and remove branches that reflect noise or outliers
Use of decision tree: Classifying an unknown sample
3. Decision Tree Induction3. Decision Tree Induction
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age income student credit_rating buys_computer<=30 high no fair no<=30 high no excellent no31…40 high no fair yes>40 medium no fair yes>40 low yes fair yes>40 low yes excellent no31…40 low yes excellent yes<=30 medium no fair no<=30 low yes fair yes>40 medium yes fair yes<=30 medium yes excellent yes31…40 medium no excellent yes31…40 high yes fair yes>40 medium no excellent no
This follows an example from Quinlan’s ID3
Training DatasetTraining Dataset
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age?
overcast
student? credit rating?
no yes fairexcellent
<=30 >40
no noyes yes
yes
30..40
Output:Output:A Decision Tree for Credit ApprovalA Decision Tree for Credit Approval
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4. Neural Networks4. Neural Networks
Advantages prediction accuracy is generally high robust, works when training examples
contain errors output may be discrete, real-valued, or a
vector of several discrete or real-valued attributes
fast evaluation of the learned target function Criticism
long training time difficult to understand the learned function not easy to incorporate domain knowledge
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The n-dimensional input vector x is mapped into variable y by means of the scalar product and a nonlinear function mapping
k-
f
weighted sum
Inputvector x
output y
Activationfunction
weightvector w
w0
w1
wn
x0
x1
xn
A NeuronA Neuron
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Multi-Layer PerceptronMulti-Layer Perceptron
INPUTLAYER
(4 Neurons)
HIDDENLAYER I(4 PEs)
HIDDENLAYER II(3 PEs)
OUTPUTLAYER
(2 Neurons)
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Applications of Neural NetworksApplications of Neural Networks
Financial Decision making Fraud Detection Bankruptcy Problem Weather Forcasting Feature Detection Voice Recognition