IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 Knowledge-Driven Business Intelligence Systems: Part II Week 11 Dr. Jocelyn San Pedro School of Information

IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004

Knowledge-Driven Knowledge-Driven Business Intelligence Business Intelligence Systems: Part IISystems: Part II

Week 11Dr. Jocelyn San PedroSchool of Information

Management & SystemsMonash University

IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 2

Lecture OutlineLecture Outline Data Mining Technologies

Neural Networks Genetic Algorithms Fuzzy Logic Decision Trees

Data Visualisation


Learning ObjectivesLearning ObjectivesAt 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


Data Mining TechnologiesData Mining Technologies1960s – 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


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


Neural NetworksNeural 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 model’s own mistakes – (Dhar and Stein, 1997)

Good at modelling poorly understood problems for which sufficient data can be collected


Artificial Neural Nets Artificial Neural Nets (ANNs)(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


Artificial Neural Nets Artificial Neural Nets (ANNs)(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 data1. The internal or hidden layer processes the data.2. The output layer relays the final result of the net.

Output Layer

Hidden Layer

Input Layer

Guesses

Processing

Data Input

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


Artificial Neural Nets Artificial Neural Nets (ANNs)(ANNs)


Make initial guess based on current weight settings and inputs

Calculate error with associated output

Determine the amount and direction of individual weight adjustment

Adjust individual weights according to calculations

Calculate error/adjust weights for each node in hidden layer

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.


Artificial Neural Nets Artificial Neural Nets (ANNs)(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

http://www.alyuda.com/


Artificial Neural Nets Artificial Neural Nets (ANNs)(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.


ANN Applications – Alyuda ANN Applications – Alyuda ForecasterForecaster Credit Approval - determine risk of granting a loan to

an applicant

Classify applicant as either LOW risk, HIGH riskGuide 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 employee’s retention probability as LOW or HIGH probability

Identify employees who intend to leave and take the appropriate measures to retain them.

www.alyuda.com



ANN Applications – Alyuda ANN Applications – Alyuda ForecasterForecaster


ANN Applications – Alyuda ANN Applications – Alyuda ForecasterForecaster 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



Data Mining TechnologiesData Mining Technologies

Genetic Algorithms Recognise a good solution, spreads some of that

solution’s features into a population of competing solutions, and “breeds” good solutions

Powerful technique for solving various combinatorial or optimisation problems

Sample Genetic algorithm online demoshttp://math.hws.edu/xJava/GA/

http://math.hws.edu/xJava/GA/


Genetic AlgorithmGenetic 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.


Genetic Algorithm - Genetic Algorithm - ExampleExampleSelecting 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.php3#stat_package



Genetic Algorithm - Genetic Algorithm - ExampleExampleThe 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.




Genetic Algorithms- Weak Genetic Algorithms- Weak PointsPoints 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.




Fuzzy LogicFuzzy 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



The Basics of Fuzzy LogicThe 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



Membership Function Example

The “Tallness” function takes a person’s 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


0.00

0.50

1.00

0 1 2 3 4 5 6 7 8 9 10

Height in Feet

Degree of

Tallness


Inferencing using Fuzzy RulesExample “Well if you’ve 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, it’s a good candidate for allocation of promotion dollars.

But you also want to promote products more heavily when they’re relatively new in order to increase market awareness and to establish market share…”

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


Inferencing using Fuzzy RulesOne fuzzy rule: If product is new, then a

client should spend more money promoting it new-product-rule

Product is NEW

Promotion should be HIGH

THEN



Inferencing using Fuzzy Rules

1

0.3

00 235 365

Days since product was introduced

- Degree of Membership in the fuzzy set NEW




Low Medium High

0 3 5 8 15 Expense as a percentage of sales

1

0.75

0

PROMOTION

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.




Low Medium High

0 1 2 3 4 5 Input

1

0.4

0.1

0

Price Sensitivity

(ratio of % change in volume per change in price)Price sensitivity is 0.4 LOW or 0.1 Medium

Take Max value or Fuzzy Set Union: Price sensitivity is 0.4 LOWDhar, V. and Stein, R. (1997)


Inferencing using Fuzzy RulesOther 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



Fuzzy SystemsFuzzy SystemsSome 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


Fuzzy SystemsFuzzy SystemsSome Disadvantages Saturation of fuzzy sets – fuzzy sets get so full

of inferences that the consequent fuzzy regions are overloaded > system loses the information provided by the fuzzy rules

Needs domain expertise to setup fuzzy sets Only provides approximation to human

reasoning


Notes on Decision TreesNotes on Decision TreesCART – 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/

http://www.salford-systems.com/


Data VisualisationData VisualisationFor any kind of high dimensional data set,

displaying predictive relationships is a challenge.

http://www.sapdesignguild.org/editions/edition2/info_zoom.asp


Human Visual Perception and Human Visual Perception and Data VisualisationData 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.



Global Private Network Global Private Network ActivityActivity

High Activity

Low Activity



Natural Gas Pipeline Natural Gas Pipeline AnalysisAnalysis

Note: Height shows total flow through compressor stations.



An “Enlivened” Risk An “Enlivened” Risk Analysis ReportAnalysis Report



Telephone Polling ResultsTelephone Polling Results

Note: On the “live” map, clicking on an area allows the user to drill down and see results for smaller areas.



ReferencesReferencesDhar, 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/vol4/sbaa/report.fuzzysets.html


Questions?

[email protected] of Information Management and

Systems, Monash UniversityT1.28, T Block, Caulfield Campus

9903 2735

mailto:[email protected]

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IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1, 2004 Knowledge-Driven Business Intelligence Systems: Part II Week 11 Dr. Jocelyn San Pedro School of Information