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Data Mining Techniques

by anupmaurya
3 minutes read

In this article, you’ll learn about Data Mining Techniques such as Classification, Clustering, Regression, Association Rules, Sequential Patterns, Prediction and more.

Data mining is the process of finding patterns in large data sets using methods that combine machine learning, statistics, and database systems. It can help organizations understand past events. 

Data Mining Techniques

Data Mining Techniques

Data Mining Techniques are as follows:

1. Classification

This analysis is used to retrieve important and relevant information about data, and metadata. This data mining method helps to classify data in different classes.

2. Clustering

Clustering analysis is a data mining technique to identify data that are like each other. This process helps to understand the differences and similarities between the data.

3. Regression

Regression analysis is the data mining method of identifying and analyzing the relationship between variables. It is used to identify the likelihood of a specific variable, given the presence of other variables.

4. Association Rules

This data mining technique helps to find the association between two or more Items. It discovers a hidden pattern in the data set.

5. Outer detection

This type of data mining technique refers to observation of data items in the dataset which do not match an expected pattern or expected behavior. This technique can be used in a variety of domains, such as intrusion, detection, fraud or fault detection, etc. Outer detection is also called Outlier Analysis or Outlier mining.

6. Sequential Patterns

This data mining technique helps to discover or identify similar patterns or trends in transaction data for certain period.

7. Prediction

Prediction has used a combination of the other techniques of data mining like trends, sequential patterns, clustering, classification, etc. It analyzes past events or instances in a right sequence for predicting a future event.

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