NCA-GENL Question 79
Select 2A data science team is working with a large dataset containing millions of customer transaction records. They aim to identify hidden purchasing patterns and visualize the trends to guide business decisions. Which combination of techniques is most appropriate for extracting insights from this dataset?
- A
Perform clustering to group customers with similar purchasing behaviors.
- B
Use regression analysis to predict future purchasing amounts for individual customers.
- C
Generate a word cloud to visualize the most commonly purchased products.
- D
Apply association rule mining to uncover relationships between purchased products.
- E
Directly use raw data without preprocessing to create a sales trend dashboard.
Show answer and explanation
Correct answers: A, D
Explanation
To extract insights from large transactional datasets, techniques like clustering and association rule mining are highly effective. Clustering helps segment customers based on behavior, while association rule mining uncovers relationships between purchased items. These methods support data-driven decisions by revealing hidden patterns. Using raw data without preprocessing or relying on unrelated techniques like word clouds is less effective for this specific case.
- A. Correct.
Clustering helps group customers with similar behaviors, which is a critical step in uncovering patterns within large datasets. This is a suitable data mining technique for such scenarios.
- B. Incorrect.
Regression analysis is predictive rather than descriptive. While it can help in forecasting, it does not directly uncover hidden patterns or trends in purchasing behavior.
- C. Incorrect.
Word clouds are primarily used for textual data visualization and are not effective for numerical or transactional datasets.
- D. Correct.
Association rule mining is specifically designed to identify relationships between items in large datasets, such as frequently purchased product combinations. It is highly relevant for this scenario.
- E. Incorrect.
Using raw data without preprocessing can lead to inaccurate or meaningless results. Data cleaning and preprocessing are essential steps before analysis or visualization.