Data Mining

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Data MiningData Mining “Application of Information and Communication Technology to

Production and Dissemination of Official statistics”

10 May – 11 July 2006

M Q HasanLecturer/ StatisticianUN Statistical Institute for Asia and the PacificChiba, JapanEmail : [email protected]

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ObjectivesObjectives

Understanding data mining

Basis for future planning and development

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ContentsContents What is data mining

Evolution of data mining

Technology and techniques involved

Software packages

References

Exercises

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What is “data mining” :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” :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” ….What is “data mining” ….

Prediction of future events, behaviors, estimating value etc.– Accuracy.

Confidence level.

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What is “data mining” ….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” ….What is “data mining” ….

Requirements–Data

–Concepts

–Instances

–Parameters

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What is NOT data mining :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 ? …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)

“What’s likely to happen to Boston unit sales next month? Why?”

Advanced algorithms, multiprocessor computers, massive databases

Prospective, proactive information delivery

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ToolsTools

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 = 11 + 1 = 1

Is it possible ?

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Let a = bThen a2 = abThen 2a2 = a2 + abThen 2a2 – 2ab = a2 – abThen 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 techniquesThinking 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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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 techniquesThinking Hat techniquesBlack 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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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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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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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 :Some DM terms : Instances

Attributes

Objects

Class

Relationships

Rule indications

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Machine learning

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Some DM techniques :Some DM techniques : Decision Trees

Neural Networks

Genetic Algorithms

Nearest neighbor methods

Rule indications

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Some DM techniquesSome DM techniquesDecision trees

– Tree shaped structure with branches

– 2 main types:Classification trees label records and assign them to the

proper classRegression trees estimate the value of a target variable

– Various algorithmsChi square automatic interaction detection (CHAID)Classification & regression trees (CART) Etc

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Some DM techniquesSome 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 techniquesSome DM techniques Genetic Algorithms

– Optimization techniques

Genetic combinations

Natural selections

Concepts of evolution

Etc

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Some DM techniquesSome DM techniques Nearest neighbor methods

– K-nearest neighbor technique

– Classification trees based on combination of classes

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Some DM techniquesSome DM techniques

Rule indications

– Extraction of if , then , else rules from data based on statistical significance

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How DM works ?How DM works ?

Modeling

– Predicting FUTURE !!!! Build once

– apply /use many

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How DM works ?How DM works ? Test validity modeling

– Known cases with known data

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Data Mining SoftwareData Mining SoftwareNumap7, 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.htm

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Data Mining SoftwareData Mining Software

Tiberius, MLP Neural Network for classification and regression problems.

– http://www.philbrierley.com/

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Eurostat-funded research projects

– SODAS – symbolic official data analysis– System => ASSO– KESO – knowledge extraction for statistical– Offices– Spin! – Spatial mining for data of public interest

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Data Mining SoftwareData 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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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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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 SoftwareData Mining SoftwareOpen 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 :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

Data Mining

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