Tag mining operators

Here are some common mining operators used in data mining and machine learning:

Filtering Operators

  1. SELECT: Selects specific columns or rows from a dataset.
  2. FILTER: Filters a dataset based on a condition or set of conditions.
  3. WHERE: Similar to FILTER, but often used in SQL queries.

Aggregation Operators

  1. SUM: Calculates the sum of a column or set of columns.
  2. AVG: Calculates the average of a column or set of columns.
  3. MAX: Returns the maximum value in a column or set of columns.
  4. MIN: Returns the minimum value in a column or set of columns.
  5. COUNT: Counts the number of rows or cells in a dataset.

Grouping Operators

  1. GROUP BY: Groups a dataset by one or more columns.
  2. PIVOT: Rotates a dataset from a tabular format to a matrix format.

Joining Operators

  1. JOIN: Combines two or more datasets based on a common column or set of columns.
  2. LEFT JOIN: Combines two or more datasets, returning all rows from the left dataset and matching rows from the right dataset.
  3. RIGHT JOIN: Combines two or more datasets, returning all rows from the right dataset and matching rows from the left dataset.
  4. FULL OUTER JOIN: Combines two or more datasets, returning all rows from both datasets.

Transforming Operators

  1. TRANSFORM: Transforms a dataset by applying a function or set of functions to each row or column.
  2. MAP: Maps a dataset from one format to another.
  3. REDUCE: Reduces a dataset by applying a function or set of functions to each row or column.

Clustering Operators

  1. K-MEANS: Divides a dataset into K clusters based on similarity.
  2. HIERARCHICAL CLUSTERING: Divides a dataset into clusters based on a hierarchical structure.

Classification Operators

  1. DECISION TREE: Builds a decision tree model to classify data.
  2. RANDOM FOREST: Builds a random forest model to classify data.
  3. SVM: Builds a support vector machine model to classify data.

Regression Operators

  1. LINEAR REGRESSION: Builds a linear regression model to predict a continuous outcome variable.
  2. LOGISTIC REGRESSION: Builds a logistic regression model to predict a binary outcome variable.

These are just a few examples of the many mining operators used in data mining and machine learning. The specific operators used often depend on the problem being solved and the type of data being analyzed.