Data Mining: Addressing Data Quality Issues

Consider a dataset containing customer purchase information. Which preprocessing technique would be most effective in reducing the impact of minor variations in product names (e.g., 'Laptop' vs. 'laptop') on association rule mining?

In a sensor network monitoring environmental conditions, some sensors occasionally transmit erroneous negative temperature readings during brief hardware glitches. Which data preprocessing technique is most appropriate to address this specific type of noise?

A hospital database contains patient records, including age, but some patients chose not to disclose their age. To avoid skewing analyses, which method is most appropriate for handling these missing values?

Two datasets, one containing customer transaction history and another containing customer demographic information from different sources, are merged. Tax ID appears in both. What is the most appropriate data preprocessing technique to ensure each customer is represented only once in the combined dataset?

You are analyzing sales data for a national retail chain. Instead of analyzing daily sales for each store, you decide to aggregate the data to monthly sales at the regional level. What is the primary benefit of this aggregation?

In a large dataset of social media posts, you want to analyze the sentiment towards a particular brand. Due to computational limitations, you cannot process the entire dataset. Which sampling method would be most appropriate to ensure that the subset accurately reflects the sentiment distribution in the entire dataset?

A dataset contains hundreds of features related to gene expression levels. Many of these features are highly correlated. Which dimensionality reduction technique would be most effective in reducing the number of features while preserving most of the variance in the data?

Consider a dataset with customer purchase history. Instead of using individual product prices, you want to create a new feature representing the price range category (e.g., low, medium, high). Which preprocessing technique is most appropriate for this transformation?

A dataset contains income values that range from $20,000 to $1,000,000. To reduce the skewness caused by high-income earners and make the data more suitable for linear regression, which attribute transformation technique would be most effective?

You want to compare the similarity between customer profiles based on their movie preferences. The dataset contains binary data indicating whether a customer has watched a particular movie (1) or not (0). Which similarity measure is most appropriate for this type of data?

A dataset contains customer reviews with varying lengths. What is the appropriate preprocessing technique to ensure reviews are similar in length before applying a text mining technique?

A dataset contains temperature readings in both Celsius and Fahrenheit. What is the appropriate preprocessing technique to make the readings comparable?

A database contains purchase history. Some clients have not agreed to share their information. What is the appropriate preprocessing technique to handle this issue?

When combining a database of Tax records with a database of PAN card records, there can be duplicates. What is the appropriate method to address this issue?

Instead of tracking individual town sales, you look at district-level sales. What kind of preprocessing is this?

When analyzing social media posts, you take a portion of the data and use that to predict overall trends. What is this called?

You have a lot of symptoms to explore when diagnosing a patient. Instead of paying attention to all of them, you should focus on only a few. What is this called?

You are analyzing age. Instead of tracking age precisely, you use bins of 10-20, 20-30, etc. What is this called?

You are analyzing values that are 10^20. To make them easier to analyze, you switch to log scale. What is this called?

You want to find the straight-line distance between two points. What is this called?

What preprocessing step addresses the issue of 'garbage in, garbage out'?

Why is preprocessing considered both a technical skill and an art?

What guides the choice of preprocessing techniques?

Which action is not a direct goal of data preprocessing?

What distinguishes outliers from noise?

Why is it important to handle missing values during preprocessing?

What is the primary risk of not addressing data duplication before data mining?

Which of the following is NOT a typical reason for performing data aggregation?

Why is choosing an appropriate sample size crucial in data sampling?

What is the main goal of dimensionality reduction techniques?

How does feature extraction differ from feature subset selection?

What is a potential drawback of discretizing continuous variables?

Why might you apply a logarithmic transformation to an attribute?

When should you use the Jaccard coefficient instead of Euclidean distance?

What is the purpose of normalization or standardization?

Which preprocessing technique is most effective for handling variations like 'USA,' 'U.S.A.,' and 'United States of America'?

Which approach is least effective for dealing with outliers?