1. Business Intelligence
Data Mining Techniques As Tools for
Business Intelligence

2. 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

3. 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.

4. 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

5. 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

6. 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)

7. 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

8. 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

9. Fraud Detection and Management (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.

10. Data Mining: A KDD Process
Data mining: the core of knowledge discovery process.

11. 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.
Deployement: Use of discovered knowledge

12. Standardized Data Mining Processes
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

13. Standardized Data Mining Processes
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

14. Standardized Data Mining Processes
Step 3: Data Preparation
Select data
Clean data
Construct data
Integrate data
Format data
Cross-Industry Standard Process for Data Mining CRISP-DM

15. Standardized Data Mining Processes
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

16. Standardized Data Mining Processes
Step 5: Evaluation
Evaluate results
Review process
Determine next step
Cross-Industry Standard Process for Data Mining CRISP-DM

17. Standardized Data Mining Processes
Step 6: Deployment
Plan deployment
Plan monitoring and maintenance
Produce final report
Review the project
Cross-Industry Standard Process for Data Mining CRISP-DM

18. Architecture of a Typical Data Mining System

19. 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”) à buys(X, “PC”) [support = 2%, confidence = 60%]
contains(T, “computer”) à contains(x, “software”)
[1%, 75%]

20. 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, 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

21. 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

22. Data Mining: Combination of Multiple Disciplines

23. 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 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.

24. Poll: Which data mining technique..?

25. 1. Association
Market Basket Analysis

26. Association 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

27. 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%]

28. 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

29. General 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

30. Examples 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

31. The K-Means Clustering Method
Example

32. 3. Decision Tree Induction
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

33. Training Dataset
This follows an example from Quinlan’s ID3

34. Output: A Decision Tree for Credit Approval
age?
<=30
overcast
30..40
>40
student?
yes
credit rating? no
yes
excellent
fair no
yes
yes no

35. 4. Neural Networks Advantages Criticism
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

36. - f A Neuron mk å weighted sum Input vector x output y Activation
function
weight
vector w å w0 w1 wn x0 x1 xn
The n-dimensional input vector x is mapped into variable y by means of the scalar product and a nonlinear function mapping

37. Multi-Layer Perceptron

38. Applications of Neural Networks
Financial Decision making
Fraud Detection
Bankruptcy Problem
Weather Forcasting
Feature Detection
Voice Recognition