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Decision Support And Business Intelligence Systems 9th Edition Dursun Delen Efraim Turban, Ramesh Sharda - Solutions
1. Define Web usage mining.
4. What are hubs and authorities? What is the HITS algorithm?
3. What are the main goals of Web structure mining?
2. Define Web structure mining, and differentiate it from Web content mining.
1. What is Web content mining? How does it differ from text mining?
3. What are the three main areas of Web mining?
2. What is Web mining? How does it differ from regular data mining?
1. What are some of the main challenges the Web poses for knowledge discovery?
3. What do you think are the pros and cons of choosing a free text mining tool over a commercial tool?
2. Why do you think most of the text mining tools are offered by statistics companies?
1. What are some of the most popular text mining software tools?
3. What are some promising text mining applications in biomedicine?
2. How can text mining be used in security and counterterrorism?
1. List and briefly discuss some of the text mining applications in marketing.
4. What are the main knowledge extraction methods from corpus?
3. What is singular value decomposition? How is it used in text mining?
2. What is the reason for normalizing word frequencies? What are the common methods for normalizing word frequencies?
1. What are the main steps in the text mining process?
4. What are the most common tasks addressed by NLP?
3. What are some of the benefits and challenges of NLP?
2. How does NLP relate to text mining?
1. What is natural language processing?
3. What are some popular application areas of text mining?
2. Why is the popularity of text mining as a BI tool increasing?
1. What is text mining? How does it differ from data mining?
5. Comment on the future of text mining tools for counterterrorism.
4. What is a summarization filter?
3. What is TopCat? What does TopCat do?
2. What is Genoa project? What is the motivation behind projects like Genoa?
1. How can text mining be used in a crisis situation?
5. What modifications would you make to improve the results of beer flavor prediction?Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands, a 20 percent share of the market, years of experience, and some of the best
4. What benefits might Coors derive if this project is successful?Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands, a 20 percent share of the market, years of experience, and some of the best people in the
3. Why were the results of Coors’ neural network initially poor, and what was done to improve the results?Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands, a 20 percent share of the market, years of experience,
2. What is the objective of the neural network used at Coors?Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands, a 20 percent share of the market, years of experience, and some of the best people in the business.
1. Why is beer flavor important to Coors’profitability?Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands, a 20 percent share of the market, years of experience, and some of the best people in the business.
11. Today’s customer has a wide variety of options regarding what he or she drinks. A drinker’s choice depends on various factors, including mood, venue, and occasion. Coors’ goal is to ensure that the customer chooses a Coors brand no matter what the circumstances are.According to Coors,
10. Go to neuralware.com. Prepare a report about the products the company offers.
9. Visit statsoft.com. Download at least three white papers of applications. Which of these applications may have used neural networks?
8. Go to easynn.com. Download the trial version of the software. After the installation of the software, find the sample file called Houseprices.tvq. Retrain the neural network and test the model by supplying some data.Prepare a report about your experience with this software.
7. Go to neuroshell.com. Look at Gee Whiz examples.Comment on the feasibility of achieving the results claimed by the developers of this neural network model.
6. Go to neoxi.com. Identify at least two software tools that have not been mentioned in this chapter. Visit Web sites of those tools and prepare a brief report on the tools’ capabilities.
5. Go to nd.com. Download the trial version of Neurosolutions for Excel and experiment with it, using one of the datasets from the exercises in this chapter. Prepare a report about your experience with the tool.
4. Go to nd.com. Read about the company’s applications in investment and trading. Prepare a report about them.
3. Go to calsci.com and read about the company’s various business applications. Prepare a report that summarizes the applications.
2. A very good repository of data that has been used to test the performance of neural network and other machinelearning algorithms can be accessed at ics.uci.edu/~mlearn/MLRepository.html. Some of the datasets are really meant to test the limits of current machine-learning algorithms and compare
1. Explore the Web sites of several neural network vendors, such as California Scientific Software (calsci.com), NeuralWare (neuralware.com), and Ward Systems Group(wardsystems.com), and review some of their products.Download at least two demos and install, run, and compare them.
5. You have worked on neural networks and other data mining techniques. Give examples of where each of these has been used. Based on your knowledge, how would you differentiate among these techniques?Assume that a few years from now you come across a situation in which neural network or other data
4. Consider the following dataset, which includes three attributes and a classification for admission decisions into an MBA program:a. Using the data given here as examples, develop your own manual expert rules for decision making.b. Build a decision tree using SPRINT (Gini index). You can build it
3. For each of the following applications, would it be better to use a neural network or an expert system? Explain your answers, including possible exceptions or special conditions.a. Diagnosis of a well-established but complex diseaseb. Price-lookup subsystem for a high-volume merchandise sellerc.
2. Build a real-world neural network. Using demo software downloaded from the Web (e.g., NeuroSolutions at neurodimension.com or another site), identify realworld data (e.g., start searching on the Web at ics.uci.edu/~mlearn/MLRepository.html or use data from an organization with which someone in
1. Consider the following set of data that relates daily electricity usage as a function of outside high temperature(for the day):a. Plot the raw data. What pattern do you see? What do you think is really affecting electricity usage?b. Solve this problem with linear regression Y = a + bX (in a
4. The purpose of this exercise is to develop a model to predict forest cover type using a number of cartographic measures. The given dataset (Online File W6.1)includes four wilderness areas found in the Roosevelt National Forest of northern Colorado. A total of 12 cartographic measures were
3. This exercise relates to the bankruptcy-prediction problem discussed in this chapter. The bankruptcy-prediction problem can be viewed as a problem of classification.The dataset you will be using for this problem includes five ratios that have been computed from the financial statements of
2. Go to the Teradata Student Network Web site (teradata studentnetwork.com) or the URL given by your instructor.Locate the Harrah’s case. Read the case and answer the following questions:a. Which of the Harrah’s data applications are most likely implemented using neural networks?b. What other
1. Go to the Teradata Student Network Web site (teradata studentnetwork.com) or the URL given by your instructor.Locate Web seminars related to data mining and neural networks. Specifically, view the seminar given by Professor Hugh Watson at the SPIRIT2005 conference at Oklahoma State University,
6. Everyone would like to make a great deal of money on the stock market. Only a few are very successful. Why is using an ANN a promising approach? What can it do that other decision support technologies cannot do? How could it fail?
5. Say that a neural network has been constructed to predict the creditworthiness of applicants. There are two output nodes: one for yes (1 = yes, 0 = no) and one for no (1 = no, 0 = yes). An applicant receives a score of 0.83 for the “yes” output node and a 0.44 for the “no”output node.
4. Explain the difference between a training set and a testing set. Why do we need to differentiate them? Can the same set be used for both purposes? Why or why not?
3. ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode.
2. The performance of ANN relies heavily on the summation and transformation functions. Explain the combined effects of the summation and transformation functions and how they differ from statistical regression analysis.
1. Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar?
4. Briefly describe a Hopfield network.
3. What is another name for a Kohonen network?
2. What is one key difference between an MLP network and a Kohonen network?
1. List some of the different types of neural networks.
5. Conduct a Web search to identify homeland security applications of neural networks.
4. What are some applications of neural networks in information security?
3. How have neural networks been used in health care?
2. What are some sports applications of neural networks?
1. List some applications of neural networks in accounting and finance.
4. Search the Internet to find other ANN explanation methods.
3. How does sensitivity analysis work?
2. Why is it important to be able to explain an ANN’s model structure?
1. What is the so-called “black-box” syndrome?
6. How did the neural network model compare with a logistic regression model in this experiment?
5. How was an output converted to bankrupt or nonbankrupt?
4. What were the network parameters for this neural network experiment?
3. Explain what is meant by resampling in this context? How was resampling used for this problem?
2. How were data divided between training and test sets for this experiment?
1. What parameters can be used to predict failure of a firm?
5. Search the Internet to identify other learning algorithms for feedforward neural networks.
4. How does error between actual and predicted outcomes affect the value of weights in neural networks?
3. What is the purpose of a learning rate and momentum?
2. What is the purpose of a threshold value in a learning algorithm?
1. Briefly describe backpropagation.
5. What is the role of the transformation function?
4. What is the role of the summation function?
3. How do weights function in an ANN?
2. Explain the following terms: neuron, axon, and synapse.
1. What is an ANN?
6. Search the Internet to locate two additional cases that use ANN to predict public opinion.
5. What were the outcomes of the study? Who can use these results? How can the results be used?
4. Why do you think ANN excels in analyzing this type of social choice problem?
3. What are the potential benefits and shortcomings of gaming/gambling for a county?
2. What factors might be used to predict public opinion toward gaming/gambling activities?Can you think of factors that are not mentioned in this case study?
1. Why is it important to study public opinion toward legalized gambling?
4. Can you think of other problems for telecommunication companies that are likely to be solved with data mining?
3. What was the problem that argonauten360°helped solve for a call-by-call provider?
2. Why was it important for argonauten360° to employ a comprehensive tool that has all modeling capabilities?
1. Why do you think that consulting companies are more likely to use data mining tools and techniques? What specific value proposition do they offer?
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