Data Mining

1
Data Mining

• Lecture 1
• Introduction to Data Mining
• Manuel Penaloza, PhD

2
Introduction to Data Mining

• Society produces huge amounts of data daily
• Retail Store
• POS data on customer purchases
• Banks
• Collection of customer service calls
• Telecommunications
• Phone call records (mobile and house-based calls)
• Medicine
• Genomic data collected on the structure of genes
• Government
• Law enforcement data, income tax data
• Others (Transactional) data from Sports,
  Schools, Research, Search engines, etc.

3
What is Data Mining (DM)?

• It is the process of discovering hidden
  relationships and patterns in large data sets
• It can also predict the outcome of a future
  observation
• Data mining is an interdisciplinary field
• It is an extension to statistical analysis
• It uses techniques from
• Statistics
• Machine learning
• Pattern recognition
• Database technology
• Visualization
• High-performance computing

4
Questions answered by DM

• Extracting useful information from a dataset that
  answer
• Which CC customers are most profitable?
• Which loan applicants are high-risk?
• Which customer will respond to a planned
  promotion?
• How do we detect phone card fraud?
• How do customer profile change over time?
• Which customers do prefer product A over product
  B?
• What is the revenue prediction for next year?
• Which students are most likely to transfer than
  others?
• Which tax payer may be cheating the system?
• Who is most likely to violate a probation
  sentence?
• What is the predicted outcome for some treatment?

5
Data sources

• Relational Databases
• Transactional data with many tables
• Data warehouses
• Historical data, aggregated and updated
  periodically
• Files
• In special format (e.g., CSV) or proprietary
  binary
• Internet or electronic mail
• HTML, XML, web search results, e-mails
• Scientific, research
• Seismology, remote sensing, etc.

6
Example Health System

• Characteristics of the Health System
• Personal medical records (GP, specialists, etc.)
• Billing records
• Hospital data (surgery, admission, etc.)
• Questions
• Are MD's following the procedures?
• Which patient may have an adverse drug
  reactions?
• Are people committing frauds?
• Which patient are most likely to get cancer?

7
Case study E-commerce

• A person buys book from Amazon.com
• Objective Recommend other books this person is
  likely to buy
• Amazon may do clustering or sequential pattern
  analysis based on books bought by other people
• Data analyzed
• Customer who bought Data Mining Practical
  Machine Learning Tools and Techniques also
  bought Introduction to Data Mining
• Recommendations have been successful for Amazon
• Increasing buyers satisfaction and purchases

8
What motivated data mining?

• Growth in data collection
• Presence of data warehouses with reliable data
• Competitive pressure to increase sales
• The development of commercial off the shelves
  (COTS) data mining software
• Examples XLMiner, Insightful Miner, SAS, SPSS
• Growth of computing power and storage capacity
• High dimensionality of the data
• Heterogeneous and complex data
• Limitation of humans

9
Insightful MinerTM 7 GUI
Figures taken from the Insightful Miner 7 Guide
10
Creating Models

• Create a network of pipelined components
• By dragging and dropping components

11
Choosing a data mining system

• They have different functionality or methodology
• Selection determined by
• Type of operating system used in your
  organization
• The data sources handle by the tool
• ASCII text files, relational databases, XML data
• The data mining functions and methods offered
• Scalability of the system
• Row and column scalability
• Visualization tools available
• Graphical user interface that guides the
  execution of the methods
• Integration with other information systems
• Cost and performance

12
Data Mining in Databases

• Current applications include data mining modules
• Example
• Database management systems such as Oracle and
  MS SQL Server
• CRM (Customer Relationship Management)
• Advantages for Database systems
• One Stop shopping
• Minimize data movement and conversion
• Disadvantages for Database systems
• Limited to DM methods available in the system
• Data extractions and transformations may not be
  powerful enough

13
Standard data mining life cycle

• CRISP (Cross-Industry Standard Process)
• It is an iterative process with phase
  dependencies
• IT consists of six (6) phases

14
CRISP_DM

• Cross-industry standard developed in 1996
• Analysts from SPSS/ISL, NCR, Daimler-Benz, OHRA
• Funding from European Commission
• Important Characteristics
• Non-proprietary
• Application/Industry neutral
• Tool neutral
• General problem-solving process
• Process with six phases but missing
• Saving results and updating the model

15
CRISP-DM Phases (1)

• Business Understanding
• Understand project objectives and requirements
• Formulation of a data mining problem definition
• Data Understanding
• Data collection
• Evaluate the quality of the data
• Perform exploratory data analysis
• Data Preparation
• Clean, prepare, integrate, and transform the
  data
• Select appropriate attributes and variables

16
CRISP-DM Phases (2)

• Modeling
• Select and apply appropriate modeling techniques
• Calibrate model parameters to optimize results
• If necessary, return to data preparation phase
  to satisfy model's data format
• Evaluation
• Determine if model satisfies objectives set in
  phase 1
• Identify business issues that have not been
  addressed
• Deployment
• Organize and present the model to the user
• Put model into practice
• Set up for continuous mining of the data

17
Data mining tasks (1)

• Classification
• Predict the categorical value of a target
  (dependent) variable based on the values of other
  attributes
• Target variable is partitioned into classes
• It predicts class membership of a new
  observation
• Examples Which drug should be prescribed for
  older patients with low sodium/potassium ratios?
• Estimation
• Similar to classification except target variable
  is numeric
• That is, predicting a numeric value
• Example Estimate the blood pressure of a person
  based on his/her age, gender, body mass index,
  etc.

18
Data mining tasks (2)

• Prediction
• Similar to estimation except that results lie in
  the future
• Example Predict the price of a stock 3 months
  into the future
• Clustering
• Grouping similar records together
• Example Find patients with similar profiles
• Associations
• Uncover rules that indicates the association
  between two or more attributes
• Find out which items are purchased together

19
Task Classification

• Build a model that learns to predict the class
  from pre-labeled instances or observations
• Many approaches Regression, Decision Trees,
  Neural Networks

Given a set of points from classes what is the
class of new point ?
Diagram taken fromwww.kdnuggets.com/data_mining_
course/index.html
20
Task Clustering

• Find grouping of instances given un-labeled data

Diagram taken fromwww.kdnuggets.com/data_mining_
course/index.html
21
DM looks easy
Regression Decision Tree Neural
Network Association Rules
Model
Data
Data Mining Method

• But it is not easy
• Real-world is complicate

22
Methods and Techniques

• Cluster Analysis (tasks clustering)
• Association Rules (tasks association)
• Decision trees (tasks prediction,
  classification)
• Neural networks (tasks prediction,
  classification)
• K-nearest neighbor (tasks prediction,
  classification, clustering)
• Regression analysis (task estimation,
  prediction)
• Confidence interval estimation (task estimation)

23
Fallacies of Data Mining (1)

• Fallacy 1 There are data mining tools that
  automatically find the answers to our problem
• Reality There are no automatic tools that will
  solve your problems while you wait
• Fallacy 2 The DM process require little human
  intervention
• Reality The DM process require human
  intervention in all its phases, including
  updating and evaluating the model by human
  experts
• Fallacy 3 Data mining have a quick ROI
• Reality It depends on the startup costs,
  personnel costs, data source costs, and so on

24
Fallacies of Data Mining (2)

• Fallacy 4 DM tools are easy to use
• Reality Analysts must be familiar with the
  model
• Fallacy 5 DM will identify the causes to the
  business problem
• Reality DM tool only identify patterns in your
  data, analysts must identify the cause
• Fallacy 6 Data mining will clean up a data
  repository automatically
• Reality Sequence of transformation tasks must
  be defined by an analysts during early DM phases
• Fallacies described by Jen Que Louie, President
  of Nautilus Systems, Inc.

25
In summary,

• Problems suitable for Data Mining
• Require to discover knowledge to make right
  decisions
• Current solutions are not adequate
• Expected high-payoff for the right decisions
• Have accessible, sufficient, and relevant data
• Have a changing environment
• IMPORTANT
• ENSURE privacy if personal data is used!
• Not every data mining application is successful!

26
Main References

• Ian Witten and Eibe Frank. Data Mining Practical
  Machine Learning Tools and Techniques, 2nd
  edition, Morgan Kaufmann Publishers
• Daniel LaRose. Discovering Knowledge in Data An
  Introduction to Data Mining, Wiley Publication
• Pang-Ning Tang et. al. Introduction to Data
  Mining, Addison Wesley
• Jiawei Han and Micheline Kamber. Data Mining
  Concepts and Techniques, Morgan Kaufmann
  Publishers
• Online data mining course offered by KDnuggetsTM
  at www.kdnuggets.com/data_mining_course/index.html
  
• Engineering Statistics Handbook available online
  at http//www.itl.nist.gov/div898/handbook/eda/sec
  tion1/eda126.htm

27
Exercise 1

• CRISP-DM is not the only DM process, do a quick
  search on the Internet for another process.
  Describe any similarity and differences with
  CRISP-DM.
• Determine how data mining could help a web search
  engine company like Google in its operation?
• Identify one or more objectives.
• Which data mining task(s) could help this
  company?