Final answer:
The data mining task focused on shrinking a large data set to a smaller one with the most important information is called data reduction or dimensionality reduction. This is essential for handling Big Data and includes methods like feature selection and data compression. An example is the Galaxy Zoo project which simplified huge astronomical data sets for scientific analysis.
Step-by-step explanation:
The data mining task that focuses on reducing a large set of data to a smaller set that contains the most critical information from the larger dataset is known as data reduction or dimensionality reduction. This task is essential when dealing with Big Data, where the volume of data is so large that it becomes challenging to process using traditional data processing techniques. Data reduction techniques aim to simplify the data without significantly losing valuable information, which can include methods such as feature selection, feature extraction, and data compression.
For example, in the Galaxy Zoo project mentioned, citizen scientists helped to classify galaxies by simplifying the vast amounts of raw data into an organized and smaller dataset that contained the most relevant information for scientific analysis. This more focused data allowed researchers to make informed conclusions and discover new types of galaxies without having to process every single piece of raw data.
Breaking down complex problems into manageable steps, such as organizing a large amount of data into categories, helps in the process of data reduction. This not only simplifies data management but also enhances our ability to interpret and analyze the data more effectively, which is an important skill in today's data-driven society.