Essential Data           Mining Algorithms           You Need to Know

10

Generates decision trees from classified data, making predictions based on attribute values. It's a supervised learning algorithm, providing easy-to-interpret results.

1

C4.5 Algorithm

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2

K-means Algorithm

Groups objects into clusters based on similarity, useful for unsupervised learning tasks like customer segmentation.

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Support Vector Machines (SVM)

3

Defines hyperplanes to separate data into classes, ideal for classification tasks where clear boundaries are needed.

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4

Apriori Algorithm

Discovers association rules among variables in a database, facilitating market basket analysis for finding related item sets.

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Expectation-Maximization Algorithm (EM)

5

Estimates parameters of statistical models with unobserved variables, aiding in data clustering without labeled class information.

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PageRank Algorithm

6

Determines the importance of objects within a network based on their links, widely used in search engine ranking.

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Boosting algorithm that combines multiple weak learners to form a strong classifier, suitable for improving classification accuracy.

Adaboost Algorithm

7

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kNN Algorithm

8

Lazy learning algorithm that classifies data based on the majority class among its nearest neighbors, useful for supervised classification tasks.

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Naive Bayes Algorithm

9

Family of classification algorithms assuming independence among features, efficient for text classification and spam filtering.

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CART Algorithm

10

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Builds decision trees for either classification or regression tasks, providing straightforward models from labeled data.

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