Stream data mining MCQs December 19, 2025November 19, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is stream data mining? (A) Mining static datasets stored in a database (B) Mining data from social media platforms (C) Mining data that arrives continuously and in real-time (D) Mining data for historical trends 2. Which of the following is a key challenge in stream data mining? (A) Handling large volumes of static data (B) Storing all the incoming data for later analysis (C) Reducing the dimensionality of non-stream data (D) Processing data in real-time with limited memory and computational resources 3. In stream data mining, what does the “Sliding Window” technique refer to? (A) Using a fixed-size subset of the incoming stream data for analysis (B) Storing only the most recent data points in memory (C) Analyzing data in time intervals based on a window that moves over the data (D) Analyzing the entire data stream regardless of its size 4. Which of the following algorithms is commonly used in stream data mining? (A) K-means clustering (with a fixed dataset) (B) Hoeffding trees (for decision tree learning in streams) (C) Decision trees for continuous data (D) Principal Component Analysis (PCA) for stream data 5. What is the key difference between stream data mining and traditional data mining? (A) Stream data mining handles a fixed, small dataset (B) Stream data mining is used only for historical analysis (C) Stream data mining processes data in real-time, while traditional data mining works on batch data (D) There is no difference between the two 6. Which of the following is a typical application of stream data mining? (A) Forecasting sales trends based on historical data (B) Categorizing customer demographics (C) Analyzing sensor data from an IoT network in real-time (D) Storing large amounts of data for later analysis 7. What does “drift” refer to in stream data mining? (A) A sudden one-time change in the stream (B) Gradual changes in the underlying data distribution over time (C) Discarding older data (D) Handling missing values 8. What is a common technique used to reduce memory requirements in stream data mining? (A) Storing all data permanently (B) Using neural networks for compression (C) Sampling the stream and keeping a subset of the data (D) Switching to batch analysis 9. In stream data mining, what is concept drift? (A) Data becoming random (B) Change in underlying data patterns requiring model adaptation (C) Removing outliers (D) Time-based movement of data 10. Which method is used to handle infinite data streams? (A) Storing all data for later processing (B) Using unlimited memory buffers (C) Ignoring new data (D) Using a time-limited sliding window 11. Which type of model is most suitable for stream data mining? (A) Static models (B) Online learning models (C) Batch learning models (D) Large fixed-memory neural networks 12. What is the role of data summarization in stream data mining? (A) Storing all data (B) Removing noise only (C) Creating compact representations for efficient processing (D) Reducing accuracy 13. Which of the following is an example of stream data? (A) Customer survey form (B) Sales database of the past year (C) Temperature readings from a sensor every minute (D) Customer contact list 14. What is incremental learning in stream data mining? (A) Periodic retraining using all data (B) Learning a new model for each data point (C) Updating the model continuously as new data arrives (D) Training once on fixed data 15. In stream data mining, what is the primary concern regarding model accuracy? (A) Accuracy on historical data (B) Real-time prediction and adaptation to new patterns (C) Storing all incoming data (D) Using complex algorithms only