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Knowledge-Driven Business Intelligence Systems: Part II

Knowledge-Driven Business Intelligence Systems: Part II. Week 11 Dr. Jocelyn San Pedro School of Information Management & Systems Monash University. Lecture Outline. Data Mining Technologies Neural Networks Genetic Algorithms Fuzzy Logic Decision Trees Data Visualisation.

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Knowledge-Driven Business Intelligence Systems: Part II

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  1. Knowledge-Driven Business Intelligence Systems: Part II Week 11 Dr. Jocelyn San PedroSchool of Information Management & Systems Monash University IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004

  2. Lecture Outline • Data Mining Technologies • Neural Networks • Genetic Algorithms • Fuzzy Logic • Decision Trees • Data Visualisation IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 2

  3. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 3

  4. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 4

  5. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 5

  6. 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 model’s own mistakes – (Dhar and Stein, 1997) • Good at modelling poorly understood problems for which sufficient data can be collected IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 6

  7. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 7

  8. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 8 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  9. 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 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. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 9 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 10

  11. 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. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 11

  12. 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 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 12

  13. ANN Applications – Alyuda Forecaster IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 13

  14. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 14

  15. Data 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 demos http://math.hws.edu/xJava/GA/ IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 15

  16. 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. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 16

  17. 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.php3#stat_package IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 17

  18. Genetic Algorithm - Example The process of evolution of population of organisms is driven by three mechanisms: • selection of the strongest – or survival of the fittestthose 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.php3#stat_package IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 18

  19. 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.php3#stat_package IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 19

  20. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 20 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  21. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 21 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  22. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 22 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  23. Inferencing using Fuzzy Rules Example “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) IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 23

  24. new-product-rule Product is NEW THEN Promotion should be HIGH 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) IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 24

  25. 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) IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 25

  26. 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) IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 26

  27. 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) IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 27

  28. 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) IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 28

  29. 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 29

  30. Fuzzy Systems Some 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 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 30

  31. 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/ IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 31

  32. Data Visualisation For any kind of high dimensional data set, displaying predictive relationships is a challenge. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 32 http://www.sapdesignguild.org/editions/edition2/info_zoom.asp

  33. 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. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 33 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  34. Global Private Network Activity High Activity Low Activity IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 34 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  35. Natural Gas Pipeline Analysis Note: Height shows total flow through compressor stations. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 35 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  36. An “Enlivened” Risk Analysis Report IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 36 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. IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 37 Marakas, G.M. (2002) Decision support systems in the 21st Century. 2nd Ed, Prentice Hall

  38. 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/vol4/sbaa/report.fuzzysets.html IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 38

  39. Questions? Jocelyn.sanpedro@sims.monash.edu.au School of Information Management and Systems, Monash University T1.28, T Block, Caulfield Campus 9903 2735 IMS3001 – BUSINESS INTELLIGENCE SYSTEMS – SEM 1 , 2004 39

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