Data Mining Techniques

- Overview

Here are some data mining techniques: 

• Decision tree: A technique that represents a data set as nodes, with each node representing a category. Decision trees are used to find patterns in large data sets. 
• Outlier detection: A technique that identifies data points that deviate significantly from the normal behavior of the dataset. The goal is to identify anomalies or outliers that may indicate errors, fraud, or unusual behavior. 
• Association rule learning: A technique that tracks patterns based on if/then statements. These statements reveal the association between the individual data sets. 
• Sequential pattern mining: A technique that discovers patterns or sequences of events that frequently occur together in a dataset. It aims to identify temporal or time-dependent relationships between variables or events. 
• Visualization: A technique used to determine data patterns. It is used in the initial phase of the data mining process and helps to discover hidden patterns. 
• Data cleaning: A technique that involves cleaning, formatting, and analyzing raw data so it can be useful and applied to different types of analytical methods. 
• Outlier analysis: A crucial step in data mining that helps to ensure that data analysis and modeling results are accurate and meaningful. 
• Naive Bayes classification: A simple classification method that predicts the classification of incoming data using historical data. 
• Association rule mining 

Data mining is a subset of data science that involves analyzing large amounts of data to find patterns, trends, and correlations. Data mining tasks and patterns can be categorized into three main groups: Prediction, Association, Clustering.

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