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Previous editions. Online Instructor Solutions Manual. Relevant Courses. Data Mining Computer Science. View larger. Request a copy. Alternative formats. Introduction to Data Mining, 2nd Edition , gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals.
Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps students understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.
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If you're interested in creating a cost-saving package for your students, contact your Pearson rep. He received his M. His research interests focus on the development of novel data mining algorithms for a broad range of applications, including climate and ecological sciences, cybersecurity, and network analysis.
Language: English. Brand new Book. Introducing the fundamental concepts and algorithms of data mining Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals.
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In any case, unless there is a good reason for eliminating the variable prior to modeling, then we should probably allow the modeling process to identify which variables are predictive and which are not. For example, Figures 3. However, a t-test see Chapter 4 for the difference in mean number of international calls for churners and non-churners is statistically significant Figure 3. Thus, had we omitted International Calls from the analysis based on the seeming lack of graphical evidence, we would have committed a mistake, and our predictive model would not perform as well.
A hypothesis test, such as this t-test, represents statistical inference and model building, and as such lies beyond the scope of exploratory data analysis. We mention it here merely to underscore the importance of not omitting predictors merely because their relationship with the target is nonobvious using EDA.
Churn False True Count. We next turn to an examination of possible multivariate associations of numeric variables with churn, using scatter plots. Multivariate graphics can uncover new interaction effects which our univariate exploration missed.
Note the straight-line partitioning off the. Records above this diagonal line, representing customers with both high day minutes and high evening minutes, appear to have a higher proportion of churners than records below the line. The univariate evidence for a high churn rate for high evening minutes was not conclusive Figure 3. Churners and non-churners are indicated with large and small circles, respectively.
Consider the records inside the rectangle partition shown in the scatter plot, which indicates a high churn area in the upper left section of the graph. These records rep- resent customers who have a combination of a high number of customer service calls and a low number of day minutes used. Note that this group of customers could not have been identified had we restricted ourselves to univariate exploration exploring variable by single variable.
This is because of the interaction between the variables. In general, customers with higher numbers of customer service calls tend to churn at a higher rate, as we learned earlier in the univariate analysis. However, Figure 3. The customers in the upper right of the scatter plot exhibit a lower churn rate than those in the upper left. But how do we quantify these graphical findings? Graphical EDA can uncover subsets of records that call for further investigation, as the rectangle in Figure 3.
Let us examine the records in the rectangle more closely. Here we select the records within the rectangular box in the upper left. Compare this to the records with high customer service calls and high day minutes essentially the data points to the right of the rectangle.
To summarize the strategy we implemented here. Generate multivariate graphical EDA, such as scatter plots with a flag overlay. Use these plots to uncover subsets of interesting records. Quantify the differences by analyzing the subsets of records. Exploratory data analysis will sometimes uncover strange or anomalous records or fields which the earlier data cleaning phase may have missed. Consider, for example, the area code field in the present data set.
Though the area codes contain numerals, they can also be used as categorical variables, since they can classify customers according to geographic location. Now, this would not be anomalous if the records indicated that the customers all lived in California. However, as shown in the contingency table in Figure 3. Also, the chi-square test see Chapter 5 has a p-value of 0. Now, it is possible that domain experts might be able to explain this type of behavior, but it is also possible that the field just contains bad data.
We should therefore be wary of this area code field, and should not include it as input to the data mining models in the next phase. Further, the state field may be in error as well. Either way, further communication with someone familiar with the data history, or a domain expert, is called for before inclusion of these variables in the data mining models.
Chapter 2 discussed four methods for binning numerical variables. Here, we pro- vide two examples of the fourth method: Binning based on predictive value. We may therefore decide to bin the customer service calls variable into two classes, low fewer than four and high four or more. This binning of customer service calls created a flag variable with two values, high and low. Our next example of binning creates an ordinal categorical variable with three values, low, medium, and high. Recall that we are trying to determine whether there is a relationship between evening minutes and churn.
Can we use binning to help tease out a signal from this noise? We reproduce Figure 3. Binning is an art, requiring judgment. Where can I insert boundaries between the bins that will maximize the difference in churn proportions?
The first boundary. Analysts may fine tune these boundaries for maximum contrast, but for now these boundary values will do just fine; remember that we need to explain our results to the client, and that nice round numbers are more easily explained. These boundaries thus define three bins, or categories, shown in Table 3. Did the binning manage to tease out a signal? Recall that the baseline churn rate for all customers is The medium group comes in very close to this baseline rate, However, the high evening minutes group has nearly double the churn proportion compared to the low evening minutes group, The chi-square test Chapter 4 is significant, meaning that these results are most likely real and not due to chance alone.
In other words, we have succeeded in teasing out a signal from the evening minutes versus churn relationship. TABLE 3. Strictly speaking, deriving new variables is a data preparation activity. However, we cover it here in the EDA chapter to illustrate how the usefulness of the new derived variables in predicting the target variable may be assessed. The resulting contingency table is shown in Table 3. Compare the results with those from Table 3. The results are exactly the same, which is not surprising, since those without the plan can have no voice mail messages.
Recall Figure 3. It would be nice to quantify this claim. We do so by selecting the records in the upper right, and compare their churn rate to that of the other records.
However, this method is ad hoc, and not portable to a different data set say the validation set. A better idea is to 1. Estimate the equation of the straight line and 2. Use the equation to separate the records, via a flag variable. This method is portable to a validation set or other related data set. We estimate the equation of the line in Figure 3. The resulting contingency table Table 3. On the other hand, this These examples illustrate the flexibility of the CRISP-DM standard practice or indeed any well-structured standard practice of data mining.
The assorted phases are interdependent, and should not be viewed as isolated from each other. For example, deriving variables is a data preparation activity, but derived variables need to be explored using EDA and sometimes significance tests.
However, some data analysts fall victim to the opposite problem, interminably iter- ating back and forth between data preparation and EDA, getting lost in the details, and never advancing toward the research objectives. When this happens, CRISP-DM can serve as a useful road map, a structure to keep the data miner organized and moving toward the fulfillment of the research goals. Suppose we would like to derive a new numerical variable which combines Customer Service Calls and International Calls, and whose values will be the mean of the two fields.
Now, since International Calls has a larger mean and standard deviation than Customer Service Calls, it would be unwise to take the mean of the raw field values, since International Calls would thereby be more heavily weighted.
Instead, when combining numerical variables, we first need to standardize. Two variables x and y are linearly correlated if an increase in x is associated with either an increase in y or a decrease in y.
The correlation coefficient r quantifies the strength and direction of the linear relationship between x and y.
At best, using correlated variables will overemphasize one data component; at worst, using correlated variables will cause the model to become unstable and deliver unreliable results. However, just because two variables are correlated does not mean that we should omit one of them.
Identify any variables that are perfectly correlated i. Do not retain both variables in the model, but rather omit one. Identify groups of variables that are correlated with each other.
Then, later, during the modeling phase, apply dimension reduction methods, such as principal components analysis1 , to these variables. Note that this strategy applies to uncovering correlation among the predictors alone, not between a given predictor and the target variable. Turning to our data set, for each of day, evening, night, and international, the data set contains three variables, minutes, calls, and charge.
The data description indicates that the charge variable may be a function of minutes and calls, with the result that the variables would be correlated. We investigate using a matrix plot Figure 3. There does not seem to be any relationship between day minutes and day calls, nor between day calls and day charge. This we find to be rather odd, as one may have expected that, as the number of calls increased, the number of minutes would tend to increase and similarly for charge , resulting in a positive correlation between these fields.
However, the graphical evidence in Figure 3. On the other hand, there is a perfect linear relationship between day minutes and day charge, indicating that day charge is a simple linear function of day minutes only. Note from Figure 3. Since day charge is perfectly correlated with day minutes, then we should eliminate one of the two variables. We do so, arbitrarily choosing to eliminate day charge and retain day minutes.
Investigation of the evening, night, and international components reflected similar findings, and we thus also eliminate evening charge, night charge, and international charge. Note that had we proceeded to the modeling phase without first uncovering these correlations, our data mining and statistical models may have returned incoherent results, due, for example, to multicollinearity in multiple regression.
We have therefore reduced the number of predictors from 20 to 16 by eliminating one of each pair of perfectly correlated predictors. A further benefit of doing so is that the dimensionality of the solution space is reduced, so that certain data mining algorithms may more efficiently find the globally optimal solution.
After dealing with the perfectly correlated predictors, the data analyst should turn to Step 2 of the strategy, and identify any other correlated predictors, for later handling with principal component analysis. The correlation of each numerical pre- dictor with every other numerical predictor should be checked, if feasible.
Correla- tions with small p-values should be identified. A subset of this procedure is shown here in Figure 3. Note that the correlation coefficient 0. The data analyst should note this, and prepare to apply principal component analysis during the modeling phase.
Let us consider some of the insights we have gained into the churn data set through the use of exploratory data analysis. We have examined each of the variables here and in the exercises , and have taken a preliminary look at their relationship with churn.
Day Minutes. Insights with respect to churn: r Customers with the International Plan tend to churn more frequently. However, these variables are still retained for input to downstream data mining models and techniques. Note the power of exploratory data analysis. We have not applied any high powered data mining algorithms yet on this data set, such as decision trees or neural network algorithms. Yet, we have still gained considerable insight into the attributes that are associated with the customers leaving the company, simply by careful application of exploratory data analysis.
These insights can be easily formulated into actionable recommendations, so that the company can take action to lower the churn rate among its customer base. Percent False. Partition data churn. Calls, churn. Blake, C. Accessed March 17, Why do we need to perform exploratory data analysis? Why should not we simply proceed directly to the modeling phase and start applying our high powered data mining software?
Why do we use contingency tables, instead of just presenting the graphical results? How can we find the marginal distribution of each variable in a contingency table?
What is the difference between taking row percentages and taking column percentages in a contingency table? What is the graphical counterpart of a contingency table? Describe what it would mean for interaction to take place between two categorical vari- ables, using an example.
What type of histogram is useful for examining the relationship between a numerical predictor and the target? Explain one benefit and one drawback of using a normalized histogram. Should we ever present a normalized histogram without showing its nonnormalized counterpart? Explain whether we should omit a predictor from the modeling stage if it does not show any relationship with the target variable in the EDA stage, and why. Describe how scatter plots can uncover patterns in two dimensions that would be invisible from one-dimensional EDA.
Make up a fictional data set attributes with no records is fine with a pair of anomalous attributes. Describe how EDA would help to uncover the anomaly. Explain the objective and the method of binning based on predictive value.
Why is binning based on predictive value considered to be somewhat of an art? What step should precede the deriving of a new numerical variable representing the mean of two other numerical variables?
What does it mean to say that two variables are correlated? Describe the possible consequences of allowing correlated variables to remain in the model. A common practice among some analysts when they encounter two correlated predictors is to omit one of them from the analysis. Is this practice recommended? Describe the strategy for handing correlated predictor variables at the EDA stage.
For each of the following descriptive methods, state whether it may be applied to categorical data, continuous numerical data, or both. Bar charts b. Histograms c. Summary statistics d.
Crosstabulations e. Correlation analysis f. Scatter plots g. Web graphs h. Using the churn data set, develop EDA which shows that the remaining numeric variables in the data set apart from those covered in the text above indicate no obvious association with the target variable. Use the Adult data set from the book series website for the following exercises. The target variable is income, and the goal is to classify income based on the other variables.
Which variables are categorical and which are continuous? Using software, construct a table of the first 10 records of the data set, in order to get a feel for the data.
Investigate whether we have any correlated variables. For each of the categorical variables, construct a bar chart of the variable, with an overlay of the target variable. Normalize if necessary. Discuss the relationship, if any, each of these variables has with the target variables. Which variables would you expect to make a significant appearance in any data mining classification model we work with? For each pair of categorical variables, construct a crosstabulation.
Discuss your salient results. If your software supports this. Construct a web graph of the categorical variables. Fine tune the graph so that interesting results emerge. Discuss your findings. Report on whether anomalous fields exist in this data set, based on your EDA, which fields these are, and what we should do about it. Report the mean, median, minimum, maximum, and standard deviation for each of the numerical variables. Construct a histogram of each numerical variables, with an overlay of the target variable income.
For each pair of numerical variables, construct a scatter plot of the variables. Based on your EDA so far, identify interesting sub-groups of records within the data set that would be worth further investigation. Apply binning to one of the numerical variables.
Do it in such a way as to maximize the effect of the classes thus created following the suggestions in the text. Now do it in such a way as to minimize the effect of the classes, so that the difference between the classes is diminished. Refer to the previous exercise. Apply the other two binning methods equal width, and equal number of records to this same variable.
Compare the results and discuss the differences. Which method do you prefer? Summarize your salient EDA findings from the above exercises, just as if you were writing a report. Descriptions of patterns and trends often suggest possible explanations for such patterns and trends, as well as possible recommendations for policy changes.
This description task can be accomplished capably with exploratory data analysis EDA , as we saw in Chapter 3. The description task may also be performed using descriptive statistics, such as the sample proportion or the regression equation, which we learn about in Chapters 4 and 5.
Table 4. Of course, the data mining methods are not restricted to only one task each, which results in a fair amount of overlap among data mining methods and tasks. For example, decision trees may be used for classification, estimation, or prediction.
Therefore, Table 4. Description Chapter 3: Exploratory data analysis Chapter 4: Univariate statistical analysis Chapter 5: Multivariate statistical analysis. Estimation Chapter 4: Univariate statistical analysis Chapter 5: Multivariate statistical analysis. Prediction Chapter 4: Univariate statistical analysis Chapter 5: Multivariate statistical analysis. Clustering Chapter Hierarchical and k-means clustering Chapter Kohonen networks.
If estimation and prediction are considered to be data mining tasks, statistical ana- lysts have been performing data mining for over a century. In Chapters 4 and 5 we examine widespread and traditional methods of estimation and prediction, drawn from the world of statistical analysis. These methods include point estimation and confidence interval estimation for population means and proportions. We discuss ways of reducing the margin of error of a confidence interval estimate.
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