What is a typical goal of data mining applications? ans. Accelerating data retrieval speed Increasing data redundancy Reducing data storage costs Improving decision-making processes

The typical goal of data mining applications is to improve decision-making processes.

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

Here's a step-by-step explanation:

1. Accelerating data retrieval speed: While this is important, it's not the primary goal of data mining. Data mining is more about extracting valuable information from the data rather than just retrieving it quickly.

2. Increasing data redundancy: This is not a goal of data mining. In fact, data mining often involves reducing redundancy and removing duplicate information to ensure the data is as clean and as accurate as possible.

3. Reducing data storage costs: While reducing storage costs can be a benefit of data mining (as it can help identify and remove redundant data), it's not the main goal. The main goal is to extract valuable insights from the data.

4. Improving decision-making processes: This is the primary goal of data mining. By uncovering patterns and insights in large data sets, data mining helps businesses make informed decisions. This could involve identifying new market opportunities, improving customer retention strategies, or predicting future trends.