Data Mining Methods

 Data Mining involves a variety of methods to discover patterns and extract valuable information from large datasets. Here we have some of them:

1.    Classification: This method is about finding a model or function that helps in separating the data into multiple categorical classes.

2.    Clustering: A technique that involves grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

3.    Association Rule Learning: Often used for market basket analysis, this method finds interesting associations and relationships among large sets of data items.

4.    Regression: This method is used to find a function that models the data with the least error.

5.    Prediction: It is one of the most valuable data mining techniques, as it involves the use of some variables or fields in the dataset to predict unknown or future values of other variables of interest.

6.    Sequential Patterns: This method is about finding statistically relevant patterns between data examples where the values are delivered in sequences.

7.    Decision Trees: A decision tree is a predictive model which maps observations about an item to conclusions about the item’s target value.

8.    Neural Networks: Inspired by the structure and functions of biological neural networks, this method is used for estimating or approximating functions that depend on a large number of inputs.

9.    Outlier Detection: In this method, the main focus is on discovering the rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.

10.  Time Series Analysis: This involves analysing time-ordered data points to extract meaningful statistics and other characteristics of the data.

These methods are powerful tools for organizations to understand their data and make informed decisions based on the insights derived from the analysis.

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