UPLOAD ERG DATA TO CONCEPT2 LOGBOOK MANUAL

Binary, qualitative, ordinal (b) Brightness as measured by a light meter. Discrete, quantitative, ratio (a) Time in terms of AM or PM. Some cases may have more than one interpretation, so briefly indicate your reasoning if you think there may be some ambiguity. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or ratio). Classify the following attributes as binary, discrete, or continuous. While it can be dangerous to draw conclusions from such a small sample, the two fields seem to contain essentially the same information. Can you tell from the three lines of sample data that are shown why she says that? Field 2 Field 3 7 for the values displayed. In the initial example of Chapter 2, the statistician says, Yes, fields 2 and 3 are basically the same. No (e) Names and addresses collected from the Web. No (d) Names and addresses of people from the telephone book.

Yes (c) Images from Earth-orbiting satellites. (a) Census data collected from No (b) IP addresses and visit times of Web users who visit your Website. For each of the following data sets, explain whether or not data privacy is an important issue. Advertising strategies could be adjusted to take advantage of such developments.Д§ 3.

Anomaly detection techniques can discover unusual patterns of user traffic, e.g., that one subject has suddenly become much more popular. Sequential association analysis can detect that that certain queries follow certain other queries with a high probability, allowing for more efficient caching. Classification can assign results to pre-defined categories such as Sports, Politics, etc. Clustering can group results with a similar theme and present them to the user in a more concise form, e.g., by reporting the 10 most frequent words in the cluster. The following are examples of possible answers. Describe how data mining can help the company by giving specific examples of how techniques, such as clustering, classification, association rule mining, and anomaly detection can be applied. Suppose that you are employed as a data mining consultant for an Internet search engine company. (i) Extracting the frequencies of a sound wave. This is an example of the area of data mining known as classification. 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. (h) Monitoring seismic waves for earthquake activities. This could also be considered as a classification problem if we had examples of both normal and abnormal heart behavior. This would involve the area of data mining known as anomaly detection. We would build a model of the normal behavior of heart rate and raise an alarm when an unusual heart behavior occurred. (g) Monitoring the heart rate of a patient for abnormalities. We could use regression for this modelling, although researchers in many fields have developed a wide variety of techniques for predicting time series. This is an example of theД¦ 2 Chapter 1 Introduction area of data mining known as predictive modelling. We would attempt to create a model that can predict the continuous value of the stock price. (f) Predicting the future stock price of a company using historical records. However, in this specific case, solutions to this problem were developed by mathematicians a long time ago, and thus, we wouldn t consider it to be data mining. 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. Since the die is fair, this is a probability calculation.

(e) Predicting the outcomes of tossing a (fair) pair of dice. (d) Sorting a student database based on student identification numbers. (c) Computing the total sales of a company. However, predicting the profitability of a new customer would be data mining. This is an accounting calculation, followed by the application of a threshold. (b) Dividing the customers of a company according to their profitability. (a) Dividing the customers of a company according to their gender. Discuss whether or not each of the following activities is a data mining task. All rights reserved.ДЈ Contents 1 Introduction 1 2 Data 5 3 Classification: Basic Concepts and Techniques 19 4 Classification: Alternative Techniques 41 5 Association Analysis: Basic Concepts and Algorithms 65 6 Association Analysis: Advanced Concepts 91 7 Cluster Analysis: Basic Concepts and Algorithms Cluster Analysis: Additional Issues and Algorithms Anomaly Detection Avoiding False Discoveries 161 iiiДҐ 1 Introduction 1.

UPLOAD ERG DATA TO CONCEPT2 LOGBOOK MANUAL

1 Introduction to Data Mining Instructor s Solution Manual Pang-Ning Tan Michael Steinbach Anuj Karpatne Vipin Kumar Copyright c 2020 Pearson Education, Inc.

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