Top 8 Data Mining Techniques In Machine Learning

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Data mining is considered to be one of the popular terms of machine learning as it extracts meaningful information from the large pile of datasets and is used for decision-making tasks.

It is a technique to identify patterns in a pre-built database and is used quite extensively by organisations as well as academia. The various aspects of data mining include data cleaning, data integration, data transformation, data discretisation, pattern evaluation and more. 

Below, we have listed the top eight data mining techniques in machine learning that is most used by data scientists.

(The list is in alphabetical order)

1| Association Rule Learning

Association Rule Learning is one of the unsupervised data mining techniques in which an item set is defined as a collection of one or more items. It is basically a standard rule-based machine learning technique that is used to discover relationships between variables in datasets. It follows the If/Then statements and includes two main parts, which are an antecedent and a consequent. 

One of its advantages is that this technique passes a low number of the database while searching the hypothesis space. This technique is useful for solving problems like analysing the behaviour of the customers. Some of …

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