Different types of data mining?

There are a number of types of data mining available in the industry. The type to be chosen depends on the nature of the business. Below are some of them listed:

• Association: correlation is found between two or more items using this method by identification of the hidden pattern in the data set and hence is also termed as relation analysis.

There are two types of association rule: single dimensional and multidimensional association rule.

• Classification: items in the data set are distinguished into classes or groups using this method. Helps in accurate detection of entity behavior within the group. Includes two steps-learning and classification.
• Clustering Analysis: cluster of data items are made on the basis of similarities. Also called data segmentation.
• Prediction: based on the past or present trends of data sets, future is predicted in this method. This method is mostly used to combine other mining methods.
• Sequential Patterns or Pattern Tracking: helps in identification of patterns that occur over period of time which is certain.
• Decision Trees: a tree structural method with a test on the attribute being represented on each internal node, result of the tests denoted by branches, class labels held by terminal nodes, topmost node being the root node.
• Outlier Analysis or Anomaly Analysis: data items that not complying with expected pattern or behavior is identified in this method.
• Neural Network: biological neural network is the basis of this model.

References: Romero, Cristobal, and Sebastian Ventura. “Educational data mining and learning analytics: An updated survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10, no. 3 (2020): e1355.