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Introduction to Data Mining (First Edition)
Introduction to Data Mining (First Edition) Pang-Ning Tan, Michigan State University, Michael Steinbach, University of Minnesota Vipin Kumar, University of Minnesota Table of Contents Sample Chapters Resources for Instructors and Students Solution Manual Errata Introduction to data mining tan steinbach kumar pdf download Start your free trial today and explore our endless blogger.com-Ning Tan, Michael Steinbach, Vipin KumarShare book pagesEnglishPDFNot available on the Perlego appPang-Ning Tan, Michael Steinbach, Vipin KumarBook detailsTable of contentsIntroduction to Data Mining presents fundamental concepts and algorithms Download Free PDF. Download Free PDF. Introduction to Data Mining Instructor's Solution Manual Pang-Ning Tan. Andrea Chow. Download PDF. Download Full PDF Package. This paper. A short summary of this paper. 37 Full PDFs related to this paper. Read Paper. Introduction to Data Mining Instructor's Solution Manual Pang-Ning blogger.com: Andrea Chow
Introduction to data mining tan steinbach kumar pdf download
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Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Introduction to Data Mining Instructor's Solution Manual Pang-Ning Tan. Andrea Chow. Download PDF Download Full PDF Package This paper. A short summary of this paper. All rights reserved. Discuss whether or not each of the following activities is a data mining task.
a Dividing the customers of a company according to their gender. This is a simple database query, introduction to data mining tan steinbach kumar pdf download. b Dividing the customers of a company according to their prof- itability.
This is an accounting calculation, followed by the applica- tion of a threshold. However, predicting the profitability of a new customer would be data mining.
c Computing the total sales of a company. Again, this is simple accounting. d Sorting a student database based on student identification num- bers. Again, this is a simple database query. e Predicting the outcomes of tossing a fair pair of dice. Since the die is fair, this is a probability calculation. If the die were not fair, and we needed to estimate the probabilities of each outcome from the data, then this is more like the problems considered by data mining.
f Predicting the future stock price of a company using historical records. We would attempt to create a model that can predict the continuous value of the stock price. We could use regression for this modelling, although researchers in many fields have developed a wide variety of techniques for predicting time series.
g Monitoring the heart rate of a patient for abnormalities. We would build a model of the normal behavior of heart rate and raise an alarm when an unusual heart behavior occurred.
This would involve the area of data mining known as anomaly de- tection. This could also be considered as a classification problem if we had examples of both normal and abnormal heart behavior.
h Monitoring seismic waves introduction to data mining tan steinbach kumar pdf download earthquake activities. In this case, we would build a model of different types of seismic wave behavior associated with earthquake activities and raise an alarm when one of these different types of seismic activity was observed.
This is an example of the area of data mining known as classification, introduction to data mining tan steinbach kumar pdf download. i Extracting the frequencies of a sound wave. This is signal processing. Suppose that you are employed as a data mining consultant for an In- ternet search engine company. Describe how data mining can help the company by giving specific examples of how techniques, such as clus- tering, classification, association rule mining, and anomaly detection can be applied.
The following are examples of possible answers. Advertising strategies could be adjusted to take advantage of such developments. For each of the following data sets, explain whether or not data privacy is an important issue. a Census data collected from — No b IP addresses and visit times of Web users who visit your Website. Yes c Images from Earth-orbiting satellites. No d Names and addresses of people from the telephone book.
No e Names and email addresses collected from the Web, introduction to data mining tan steinbach kumar pdf download. While it can be dangerous to draw con- clusions from such a small sample, the two fields seem to contain essentially the same information. Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative nominal or ordinal or quantitative interval or ratio.
Some cases may have more than one interpretation, so briefly indicate your reasoning if you think there may be some ambiguity. Example: Age in years. Introduction to data mining tan steinbach kumar pdf download Discrete, quantitative, ratio a Time in terms of AM or PM.
Binary, qualitative, ordinal b Brightness as measured by a light meter. Continuous, quan- titative, ratio e Bronze, Silver, and Gold medals as awarded at the Olympics. Discrete, qualitative, ordinal f Height above sea level. Discrete, quantitative, ratio h ISBN numbers for books. Look up the format on the Web. Discrete, qualitative, ordinal j Military rank. Discrete, qualitative, ordinal k Distance from the center of campus.
Discrete, quan- titative, ratio m Coat check number. When you attend an event, you can often give your coat to someone who, in turn, gives you a number that you can use to claim your coat when you leave. Discrete, qualitative, nominal 3. You are approached by the marketing director of a local company, who be- lieves that he has devised a foolproof way to measure customer satisfaction. I just keep track of the number of customer complaints for each product.
I read in a data mining book that counts are ratio introduction to data mining tan steinbach kumar pdf download, and so, my measure of product satisfaction must be a ratio attribute. But when I rated the products based on my new customer satisfac- tion measure and showed them to my boss, he told me that I had overlooked the obvious, and that my measure was worthless. I think that he was just mad because our best-selling product had the worst satisfaction since it had the most complaints.
Could you help me set him straight? If you answered, his boss, what would you do to fix the measure of satisfaction? The boss is right.
total number of sales for the product b What can you say about the attribute type of the original product satisfaction attribute? Nothing can be said about the attribute type of the original measure. For example, two products that have the same level of customer satis- faction may have different numbers of complaints and vice-versa. A few months later, you are again approached by the same marketing director as in Exercise 3.
This time, he has devised a better approach to measure the extent to which a customer prefers one product over other, similar products. However, our test subjects are very indecisive, especially when there are more than two products. As a result, testing takes forever. I suggested that we perform the comparisons in pairs and then use these comparisons to get the rankings. Thus, if we have three product variations, we have the customers compare variations 1 and 2, then 2 and 3, and finally 3 and 1.
Our testing time with my new procedure is a third of what it was for the old procedure, but the employees conducting the tests complain that they cannot come up with a consistent ranking from the results. And my boss wants the latest product evaluations, yesterday. I should also mention that he was the person who came up with the old product evaluation approach. Can you help me? Will his approach work for gener- ating an ordinal ranking of the product variations in terms of customer preference?
Yes, the marketing director is in trouble. A customer may give incon- sistent rankings. For example, a customer may prefer 1 to 2, 2 to 3, but 3 to 1. More generally, what can you say about trying to create an ordinal measurement scale based on pairwise comparisons?
One solution: For three items, do only the first two comparisons. A more general solution: Put the choice to the customer as one of order- ing the product, but still only allow pairwise comparisons. In general, introduction to data mining tan steinbach kumar pdf download, creating an ordinal measurement scale based on pairwise comparison is difficult because of possible inconsistencies.
c For the original product evaluation scheme, the overall rankings of each product variation are found by computing its average over all test sub- jects. Comment on whether you think that this is a reasonable ap- proach. What other approaches might you take?
First, there is the issue that the scale is likely not an interval or ratio scale. Nonetheless, for practical purposes, an average may be good enough.
[DATA MINING] Урок 1 - Введение в Анализ Данных
, time: 1:02:53Introduction to data mining tan steinbach kumar pdf download
Download Free PDF. Download Free PDF. Introduction to Data Mining Instructor's Solution Manual Pang-Ning Tan. Andrea Chow. Download PDF. Download Full PDF Package. This paper. A short summary of this paper. 37 Full PDFs related to this paper. Read Paper. Introduction to Data Mining Instructor's Solution Manual Pang-Ning blogger.com: Andrea Chow Introduction to data mining tan steinbach kumar pdf download Start your free trial today and explore our endless blogger.com-Ning Tan, Michael Steinbach, Vipin KumarShare book pagesEnglishPDFNot available on the Perlego appPang-Ning Tan, Michael Steinbach, Vipin KumarBook detailsTable of contentsIntroduction to Data Mining presents fundamental concepts and algorithms Introduction 1. Discuss whether or not each of the following activities is a data mining task. (a) Dividing the customers of a company according to their gender. No. This is a simple database query. (b) Dividing the customers of a company according to their prof-itability. No. This is an accounting calculation, followed by the applica-tion of a
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