Sidst opdateret 21. august 2023
What does data mining mean?
Data mining is the process of analyzing a large batch of information to discern trends and patterns. Data mining can be used by corporations for everything from learning about what customers are interested in or what to buy to fraud detection and spam filtering.
What are the different techniques?
Data mining uses algorithms and various other techniques to convert large collections of data into useful output. The most popular types of data mining techniques include:
- Classification uses predefined classes to assign to objects. These classes describe the characteristics of items or represent what the data points have in common with each. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines.
- are used to classify or predict an outcome based on a set list of criteria or decisions. A decision tree is used to ask for the input of a series of cascading questions that sort the dataset based on the responses given. Sometimes depicted as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
- Clustering is similar to classification. However, clustering identifies similarities between objects, then groups those items based on what makes them different from other items. While classification may result in groups such as “hair care” and “dental health” instead of “shampoo”, “conditioner”, “soap”, etc.