Top 5 data mining algorithms in 2021


The practice of discovering patterns and relationships in massive amounts of data is known as data mining. Due to the advancement of computers, the internet, and large databases, it is now possible to collect massive amounts of data. The data acquired may be reviewed over time to help in the detection of relationships and solving problems.

It’s a powerful data analysis technique that combines machine learning and artificial intelligence to extract relevant information, allowing organizations to understand more about their customers’ preferences, raise revenues, cut expenses, strengthen customer relationships, and more.

Here are the top 5 data mining algorithms in 2021:

Apriori Algorithm

The Apriori algorithm works by learning association rules-a form of data mining approach for determining the relationships between variables in a database. The association rules learn and then applied to a database with a large number of transactions. 

The Apriori method is an unsupervised learning tool for discovering interesting patterns and connections. Although the approach is incredibly efficient, it consumes a massive amount of memory, occupies huge disc space, and takes a long time to operate.

EM Algorithm

As a clustering strategy, the Expectation-Maximization or EM algorithm is the same as the k-means algorithm for extracting knowledge. Iterative EM techniques increase the likelihood of detecting observed data. 

The statistical model’s parameters are then estimated using unobserved variables, resulting in observed data. It is once again unsupervised learning because we are using the EM approach without any specified class information.

PageRank Algorithm

PageRank is used extensively by Google and other search engines. It is a network analysis technique that determines the relative importance of things linked in a network. Link analysis is a type of network analysis that examines how items are connected. 

Google uses this algorithm to analyze the backlinks between web pages. It’s one of the methods Google uses to determine the relative importance of a webpage and rank it higher in the search engine.

CART Algorithm

CART (Classification and Regression Trees) is a decision tree algorithm that generates categorization and regression trees. In CART, each decision tree node will contain precisely two branches. 

CART is a classifier, like C4.5. The regression or classification tree model develops using the user-supplied labelled training dataset. As a result, it’s referred to as supervised learning.

K-mean Algorithm

K-means, one of the most widely used clustering algorithms, divides a set of objects into k groups based on their similarity. Although it is not certain that group members will be identical, they will be more similar than non-group members. 

According to standard implementations, K-means is an unsupervised learning method because it learns the group without external data.

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