Data Mining

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Data Mining Application of Information and
Communication Technology to Production and
Dissemination of Official statistics10 May 11
July 2006

• M Q Hasan
• Lecturer/ Statistician
• UN Statistical Institute for Asia and the Pacific
• Chiba, Japan
• Email hasan_at_unsiap.or.jp

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Objectives

• Understanding data mining
• Basis for future planning and development

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Contents

• What is data mining
• Evolution of data mining
• Technology and techniques involved
• Software packages
• References
• Exercises

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What is data mining

• The nontrivial extraction of implicit,
  previously unknown, and potentially useful
  information from data"
• The science of extracting useful information
  from large data sets or databases".
• Wikipedia, the free encyclopaedia

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What is data mining

• Also term as data discovery
• Process of analyzing data to identify patterns or
  relationship
• Extraction of pattern or information from stored
  information

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What is data mining .

• Prediction of future events, behaviors,
  estimating value etc.
• Accuracy.
• Confidence level.

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What is data mining .

• Process of data mining
• the initial exploration of available data
• model building or pattern identification with
  validation
• the application of the model to new data in order
  to generate predictions

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What is data mining .

• Requirements
• Data
• Concepts
• Instances
• Parameters

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What is NOT data mining

• Data warehousing
• SQL / ad hoc queries / reporting
• Software agents
• Online analytical processing (OLAP)
• Data visualization

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Why DM now ?

• Development and refinement of three technologies
  over the years.
• Massive data collection and storage facility.
• Databases of terabyte order.
• Includes publicly available data
• Powerful multiprocessor computers.
• Parallel processing technology, distributed
  technology, speed.
• Data mining algorithms.
• Statistical, Data Modeling etc.

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Evolutionary Step Business Question Enabling Technologies Characteristics
Data Collection (1960s) What was my total revenue in the last five years? Computers, tapes, disks Retrospective, static data delivery
Data Access (1980s) What were unit sales in New England last March? RDBMS, SQL, ODBC Retrospective, dynamic data delivery at record level
Data Warehousing Decision Support (1990s) What were unit sales in New England last March? Drill down to Boston." On-line analytic processing (OLAP), multidimensional databases, data warehouses Retrospective, dynamic data delivery at multiple levels
Data Mining (Ememrged) Whats likely to happen to Boston unit sales next month? Why? Advanced algorithms, multiprocessor computers, massive databases Prospective, proactive information delivery
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Tools

• Case based reasoning.
• Case-based reasoning tools provide a means to
  find records similar to a specified record or
  records. These tools let the user specify the
  "similarity" of retrieved records.
• Data visualization.
• Data visualization tools let the user easily and
  quickly view graphical displays of information
  from different perspectives.

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1 1 1

• Is it possible ?

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• Let a b
• Then a2 ab
• Then 2a2 a2 ab
• Then 2a2 2ab a2 ab
• Then 2(a2 ab) 1(a2 ab)
• Then (1 1)(a2 ab) 1(a2 ab)
• Canceling (a2 ab) from both sides
• 1 1 1
• Where is the FALASY ?

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• In data mining think from all sides ?
• Avoid the FALASIES

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Thinking Hat techniques

• White hat.
• With this thinking hat you focus on the data
  available. Look at the information you have, and
  see what you can learn from it. Look for gaps in
  your knowledge, and either try to fill them or
  take account of them.This is where you analyse
  past trends, and try to extrapolate from
  historical data.

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Thinking Hat techniques

• Red hat
• 'Wearing' the red hat, you look at problems using
  intuition, gut reaction, and emotion. Also try to
  think how other people will react emotionally.
  Try to understand the responses of people who do
  not fully know your reasoning.

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Thinking Hat techniques

• Black hat using black hat thinking.
• Look at all the bad points of the decision.
• Look at it cautiously and defensively.
• Try to see why it might not work.
• Helps to make plans 'tougher' and resilient.
• Help you to spot fatal flaws and risks.
• Helps sometime successful people get so used to
  thinking positively that often they cannot see
  problems in advance.

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Thinking Hat techniques

• Yellow hat using yellow hat thinking.
• Helps think positively.
• Helps you to see all the benefits of the decision
  and the value in it.
• Helps you to keep going when everything looks
  gloomy and difficult.

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Thinking Hat techniques

• Green hat the green hat stands for creativity.
• This is time to develop creative solutions to a
  problem.
• Little criticism of ideas.
• A whole range of creativity tools can help.

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Thinking Hat techniques

• Blue hat the blue hat stands for process
  control..
• This is the hat worn by people chairing meetings.
  When running into difficulties because ideas are
  running dry, they may direct activity into green
  hat thinking. When contingency plans are needed,
  they will ask for black hat thinking, etc.

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Some DM terms

• Instances
• Attributes
• Objects
• Class
• Relationships
• Rule indications

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• Machine learning

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Some DM techniques

• Decision Trees
• Neural Networks
• Genetic Algorithms
• Nearest neighbor methods
• Rule indications

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Some DM techniques

• Decision trees
• Tree shaped structure with branches
• 2 main types
• Classification trees label records and assign
  them to the proper class
• Regression trees estimate the value of a target
  variable
• Various algorithms
• Chi square automatic interaction detection
  (CHAID)
• Classification regression trees (CART)
• Etc

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Some DM techniques

• Neural Networks
• Learn through training
• Resemble to biological networks in structure
• Can produce very good predictions
• Not easy to use and to understand
• Cannot deal with missing data

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Some DM techniques

• Genetic Algorithms
• Optimization techniques
• Genetic combinations
• Natural selections
• Concepts of evolution
• Etc

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Some DM techniques

• Nearest neighbor methods
• K-nearest neighbor technique
• Classification trees based on combination of
  classes

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Some DM techniques

• Rule indications
• Extraction of if , then , else rules from data
  based on statistical significance

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How DM works ?

• Modeling
• Predicting FUTURE !!!!
• Build once
• apply /use many

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How DM works ?

• Test validity modeling
• Known cases with known data

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Data Mining Software

• Numap7, freeware for fast development,
  validation, and application of regression type
  networks including the multi layer perception,
  functional link net, piecewise linear network,
  self organizing map and k-means.
• http//www-ee.uta.edu/eeweb/ip/Software/Software.h
  tm

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Data Mining Software

• Tiberius, MLP Neural Network for classification
  and regression problems.
• http//www.philbrierley.com/

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Data Mining Software

• Eurostat-funded research projects
• SODAS symbolic official data analysis
• System gt ASSO
• KESO knowledge extraction for statistical
• Offices
• Spin! Spatial mining for data of public interest

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Data Mining Software

• SAS data mining tools
• Enterprise miner and text miner
• Applications relevant to national statistical
  offices
• Build a model of real world based on various
• Data
• Use the model to produce patterns
• Reveal trends
• Explain known outcomes
• Predict the future outcomes
• Forecast resource demands
• Identify factors to secure a desired effect
• Produce new knowledge to better inform
• Decision makers before they act
• Predict new opportunities

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Data Mining Software

• SAS data mining process A framework for data
  mining sample, explore, modify, model, assess
• Integrated models and algorithms
• Decision trees
• Neural networks
• Regression
• Memory based reasoning
• Bagging and boosting ensembles
• Two-stage models
• Clustering
• Time series
• Associations

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Data Mining Software

• SPSS Clementine
• Data mining workbench
• Applications relevant to national statistical
  offices
• Find useful relationships in large data sets
• Develop predictive models
• Improve decision making
• Modeling
• Prediction and classification neural networks,
  decision
• Trees and rule induction, linear regression,
  logistic
• Regression, multinomial logistic regression
• Clustering and segmentation Kohonen network,
  Kmeans,
• And two steps
• Association detection GRI, apriori, and sequence
• Data reduction factor analysis and principle
• Components analysis
• Meta-modeling combination of models

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Data Mining Software

• Open source data mining
• Www.Cs.waikato.Ac.nz/ml/weka - Weka (Waikato
• Environment for knowledge analysis)
• Data mining software in java
• Collection of machine learning algorithms for
  data
• Mining tasks
• Data pre-processing
• Classification
• Regression
• Clustering
• Association rules
• Visualization
• Platforms Linux, windows and Macintosh
• Apply directly to a dataset or call from java
  code
• Online documentation
• Tutorial
• User guide
• API documentation

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References

• Statistical Data Mining Tutorials
• http//www-2.cs.cmu.edu/awm/tutorials/
• Data Mining Glossary
• http//www.twocrows.com/glossary.htm
• Mind tools - Decision Tree Analysis
• http//www.mindtools.com/dectree.html
• Welcome to TheDataMine
• http//www.the-data-mine.com/
• An Introduction to Data Mining - Discovering
  hidden value in your data warehouse
• http//www.thearling.com/text/dmwhite/dmwhite.htm
• An Introduction to Data Mining
• http//www.thearling.com/dmintro/dmintro.pdf
• Data Mining for Official Statistics, Phan Tuan
  Pham (UNSD)
• SIAP ICT, Chiba, 7 9 June 2004
• Wikipedia, the free encyclopaedia
• http//en.wikipedia.org/wiki/Data_mining