KnowledgeDriven Business Intelligence Systems: Part II

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Knowledge-Driven Business Intelligence Systems
Part II

• Week 11
• Dr. Jocelyn San PedroSchool of Information
  Management Systems
• Monash University

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Lecture Outline

• Data Mining Technologies
• Neural Networks
• Genetic Algorithms
• Fuzzy Logic
• Decision Trees
• Data Visualisation

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Learning Objectives

• At the end of this lecture, the students will
• Gain some understanding of data mining
  technologies (decision trees, neural networks,
  genetic algorithms, and fuzzy logic) that are
  commonly used in data mining techniques
• Preview some visualisation tools and gain an
  understanding of how they support business
  decision making

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Data Mining Technologies

• 1960s classical statistical analysis
• Correlation, regression, chi-square,
  cross-tabulation
• 1980s classical statistical analysis augmented
  by more powerful set of soft computing techniques
• neural networks, genetic algorithms, fuzzy logic,
  decision trees

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Soft Computing

• Emerging discipline that combines computational
  methods for dealing with inexact, approximate
  reasoning approaches
• simulating the brain-way of solving problems -
  neural networks
• evolving solutions - genetic algorithms
• dealing with logical ambiguity - fuzzy logic
• representing effect of each event, or decision,
  on successive events decision trees

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Neural Networks

• Attempt to mirror the way human brain works in
  recognizing patterns by developing mathematical
  structures with the ability to learn (Marakas,
  2002)
• Attempt to learn patterns from data directly,
  by sifting data repeatedly, searching for
  relationships, automatically building models, and
  correcting over and over again the models own
  mistakes (Dhar and Stein, 1997)
• Good at modelling poorly understood problems for
  which sufficient data can be collected

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Artificial Neural Nets (ANNs)

• simple computer programs that build models from
  data by trial and error
• Learning from Experience
• Present a piece of data to a neural network
• The net predicts an output
• The net compares is guess to the actual correct
  value (also presented to the network)
• If ANN guess is right, the net does nothing
• If ANN guess is wrong, net figures out how to
  adjust some internal parameters so that it can
  make better prediction if it sees similar data
  again in future
• Over time, the ANN begins to converge on a fairly
  accurate model of the process

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Artificial Neural Nets (ANNs)

• Network Topology- The number of layers and units
  in each layer and a way in which the units are
  connected together.
• 3 basic layers
• The input layer receives the data
• The internal or hidden layer processes the data.
• The output layer relays the final result of the
  net.

Output Layer
Guesses
Hidden Layer
Processing
Input Layer
Data Input
Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
9
Artificial Neural Nets (ANNs)
Training the ANN - adjusting neural network
weights. During training the network analyses the
data you have provided and changes weights
between network units to reflect dependencies
found in your data.
Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
10
Artificial Neural Nets (ANNs)

• Testing is a process of estimating quality of the
  trained neural network. During this process a
  part of data that wasn't used during training is
  presented to the trained network case by case.
  Then forecasting error is measured on each case
  and used as the estimation of network quality.

Preparing the ANN in Alyuda Forecaster
www.alyuda.com
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Artificial Neural Nets (ANNs)

• Effective in problems of image recognition
• Not suited well for, say, financial or serious
  medical applications.
• highly intricate systems - include dozens of
  neurons with a couple hundred connections between
  them
• non-transparency of forecasting models
  represented by a trained neural network
• knowledge reflected in terms of weights of a
  couple hundred intraneural connections cannot be
  analysed and interpreted by a human.
• Despite of these difficulties neural networks are
  actively used (with varying success) in different
  financial applications in the majority of
  developed countries.

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ANN Applications Alyuda Forecaster

• Credit Approval - determine risk of granting a
  loan to an applicant
• Classify applicant as either LOW risk, HIGH risk
• Guide decision in granting or denying new loans
• Employee retention- identify potential employees
  who are likely to stay with the organization
  during the next year based on previous year data
• Classify employees retention probability as LOW
  or HIGH probability
• Identify employees who intend to leave and take
  the appropriate measures to retain them.

www.alyuda.com
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ANN Applications Alyuda Forecaster
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ANN Applications Alyuda Forecaster

• Gas consumption - forecast gas consumption by a
  power plant.
• Sales forecasting - forecast weekly sales of a
  small restaurant chain using the historical data
  over 109 weeks period
• Stock prediction - forecast the percentage of the
  Close price change for Chevron Corp 4 days in
  advance

www.alyuda.com
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Data Mining Technologies

• Genetic Algorithms
• Recognise a good solution, spreads some of that
  solutions features into a population of
  competing solutions, and breeds good solutions
• Powerful technique for solving various
  combinatorial or optimisation problems
• Sample Genetic algorithm online demos
• http//math.hws.edu/xJava/GA/

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Genetic Algorithm

• First a population of possible solutions to a
  problem are developed.
• Next, the better solutions are recombined with
  each other to form some new solutions.
• Finally the new solutions are used to replace the
  poorer of the original solutions and the process
  is repeated.

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Genetic Algorithm - Example

• Selecting a fixed number of market parameters
  influencing the market performance the most
• names of these parameters comprise a descriptive
  set or a set of chromosomes determining qualities
  of an "organism" - a solution of the problem
• Values of parameters determining a solution
  correspond to genes
• A search for the optimal solution is similar then
  to the process of evolution of a population of
  organisms, where each organism is represented by
  a set of its chromosomes.
• http//www.megaputer.com/dm/systems.php3stat_pack
  age

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Genetic Algorithm - Example

• The process of evolution of population of
  organisms is driven by three mechanisms
• selection of the strongest or survival of the
  fittest those sets of chromosomes that
  characterise the most optimal solutions
• cross-breeding - production of new organisms by
  mixing sets of chromosomes of parent sets of
  chromosome
• mutations - accidental changes of genes in some
  organisms of the population.
• After a number of new generations built with the
  help of the described mechanisms one obtains a
  solution that cannot be improved any further.
  This solution is taken as a final one.

http//www.megaputer.com/dm/systems.php3stat_pack
age
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Genetic Algorithms- Weak Points

• The very way of formulating the problem deprives
  one of any opportunity to estimate statistical
  significance of the obtained solution.
• Second, only a specialist can develop a
  criterion for the chromosome selection and
  formulate the problem effectively.
• Thus genetic algorithms should be considered at
  present more as an instrument for scientific
  research rather than as a tool for generic
  practical data analysis, for instance, in finance.

http//www.megaputer.com/dm/systems.php3stat_pack
age
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Fuzzy Logic

• Our language is full of vague and imprecise
  concepts, and allows for conveyance of meaning
  through semantic approximations
• These approximations are useful to humans, but do
  not readily lend themselves to the rule-based
  reasoning done on computers.
• Use of fuzzy logic is how computers handle this
  ambiguity
• Allows for partial or fuzzy description of rules

Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
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The Basics of Fuzzy Logic

• In a crisp rule, the result is either false (0)
  or true (1) and can be stored in a binary
  fashion.
• In a fuzzy rule, the result ranges from 0
  (absolutely false) to 1 (absolutely true), with
  stops in between.
• absolutely false, slightly false, slightly true,
  absolutely true
• slightly similar, similar, very similar
• These operations utilise functions that assign a
  degree of membership in a set.
• Degree of similarity of current data to
  historical data is 0.75

Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
22
Membership Function Example

• The Tallness function takes a persons height
  and converts it to a numerical scale from 0 to 1.
• Here the statement He is Tall is absolutely
  false for heights below 5 feet and absolutely
  true for heights above 7 feet

Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
23
Inferencing using Fuzzy Rules

• Example
• Well if youve got a high margin, price
  sensitive product, promoting that product via
  ads, displays, etc. is likely to have a high
  impact on sales volume. If the volume impact is
  high, its a good candidate for allocation of
  promotion dollars.
• But you also want to promote products more
  heavily when theyre relatively new in order to
  increase market awareness and to establish market
  share

Dhar, V. and Stein, R. (1997)
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Inferencing using Fuzzy Rules

• One fuzzy rule If product is new, then a client
  should spend more money promoting it

Dhar, V. and Stein, R. (1997)
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Inferencing using Fuzzy Rules
? - Degree of Membership in the fuzzy set NEW
? 1 0.3 0
0 235 365 Days since product was
introduced
Dhar, V. and Stein, R. (1997)
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Inferencing using Fuzzy Rules
Promotion expense that is 2 of sales is
absolutely LOW
The degree of Lowness of Promotion expense that
is 2.9 of sales is 0.75.
PROMOTION
1 0.75 0
Low Medium High
0 3 5 8
15 Expense as a percentage of sales
Dhar, V. and Stein, R. (1997)
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Inferencing using Fuzzy Rules
Price Sensitivity (ratio of change in volume
per change in price)
Price sensitivity is 0.4 LOW or 0.1 Medium
1 0.4 0.1 0
Low Medium High
Take Max value or Fuzzy Set Union Price
sensitivity is 0.4 LOW
0 1 2 3
4 5 Input
Dhar, V. and Stein, R. (1997)
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Inferencing using Fuzzy Rules

• Other fuzzy rules
• If product is NEW, then a client should spend
  MORE money promoting it
• If the price sensitivity of product is LOW, then
  promotion should be LOW
• If the price sensitivity of product is MEDIUM,
  then promotion should be MEDIUM
• If the price sensitivity of product is HIGH, then
  promotion should be HIGH

Dhar, V. and Stein, R. (1997)
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Fuzzy Systems

• Some Advantages
• Great in dealing with qualitative data, as well
  as object attribute
• Offers an attractive trade-off between accuracy
  and compactness express relationships in terms
  of simple rules
• Not computationally expensive compared to
  crisp rule-based systems

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Fuzzy Systems

• Some Disadvantages
• Saturation of fuzzy sets fuzzy sets get so full
  of inferences that the consequent fuzzy regions
  are overloaded gt system loses the information
  provided by the fuzzy rules
• Needs domain expertise to setup fuzzy sets
• Only provides approximation to human reasoning

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Notes on Decision Trees

• CART Classification and Regression Trees
• Most common decision tree, statistical analysis
  data mining tool
• automatically searches for and finds high
  performance classification and prediction
• key elements are a set of rules for
• splitting each node in a tree
• deciding when a tree is complete and
• assigning each terminal node to a class outcome
  (or predicted value for regression)
• More info and software demo on http//www.salford-
  systems.com/

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Data Visualisation

• For any kind of high dimensional data set,
  displaying predictive relationships is a
  challenge.

http//www.sapdesignguild.org/editions/edition2/in
fo_zoom.asp
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Human Visual Perception and Data Visualisation

• Data visualisation is so powerful because the
  human visual cortex converts objects into
  information so quickly.
• The next three slides show (1) usage of global
  private networks, (2) flow through natural gas
  pipelines, and (3) a risk analysis report that
  permits the user to draw an interactive yield
  curve.
• All three use height or shading to add additional
  dimensions to the figure.

Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
34
Global Private Network Activity
High Activity
Low Activity
Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
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Natural Gas Pipeline Analysis
Note Height shows total flow through compressor
stations.
Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
36
An Enlivened Risk Analysis Report
Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
37
Telephone Polling Results
Note On the live map, clicking on an area
allows the user to drill down and see results
for smaller areas.
Marakas, G.M. (2002) Decision support systems in
the 21st Century. 2nd Ed, Prentice Hall
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References

• Dhar, V. and Stein, R. (1997) Intelligent
  decision Support Methods the Science of
  Knowledge Work, Prentice Hall.
• Dhar, V. and Stein, R. (1997) Seven methods for
  transforming corporate data into business
  intelligence.
• Marakas, G.M. (2002) Decision support systems in
  the 21st Century. 2nd Ed, Prentice Hall (or
  other editions)
• Power, D. (2002) Decision Support Systems
  Concepts and Resources for Managers, Quorum
  Books.
• Good Online resource on fuzzy sets and operations
  http//www.doc.ic.ac.uk/nd/surprise_96/journal/vo
  l4/sbaa/report.fuzzysets.html

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• Questions?
• Jocelyn.sanpedro_at_sims.monash.edu.au
• School of Information Management and Systems,
  Monash University
• T1.28, T Block, Caulfield Campus
• 9903 2735