Data cleaning is a preprocessing step that prepares the data for mining, while classification, clustering, and association rule mining are all data mining tasks.

In a decision tree, each internal node represents an attribute, while each leaf node represents a class label.

K-Means is a widely used clustering algorithm that is known for its efficiency in handling large datasets.

Association rule mining is a data mining technique that is used to find frequent patterns in a dataset.

Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables, while decision tree, neural network, and support vector machine are all types of data mining models.

Regression is a data mining technique that is used to predict the value of a continuous variable.

Anomaly detection is a data mining technique that is used to find outliers in a dataset.

Weather forecasting is not a common data mining application, while fraud detection, customer segmentation, and medical diagnosis are all common applications of data mining.

Feature selection is a data mining technique that is used to find the most influential features in a dataset.

Dimensionality reduction is a data mining technique that is used to reduce the dimensionality of a dataset.

Data transformation is a data mining technique that is used to transform the data into a more suitable format for mining.

Excel is not a common data mining tool, while RapidMiner, Weka, and MATLAB are all popular data mining tools.

Nearest neighbor search is a data mining technique that is used to find the most similar instances in a dataset.

Frequent pattern mining is a data mining technique that is used to find the most frequent patterns in a dataset.

Feature selection is a data mining technique that is used to find the most discriminative features in a dataset.