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Data mining is a field of computer science that seeks patterns and repetitions in large datasets. In Artificial Intelligence and Data Science, data mining algorithms and techniques are widely used. In this article, we have explained the top ten data mining algorithms you should be knowing in the year 2023. Read this article to know elaborately about data mining algorithms in 2023.
Ross Quinlan created C4.5, one of the top data mining algorithms. C4.5 is used to generate a classifier in the form of a decision tree from previously classified data. A classifier is a data mining tool that takes data that needs to be classified and attempts to predict the class of new data.
Each data point will have its own set of attributes. C4.5's decision tree asks a question about the value of an attribute, and the new data is classified based on the answers. C4.5 is a supervised learning algorithm because the training dataset is labelled with lasses. Because decision trees are always simple to interpret and explain, C4.5 is both fast and popular when compared to other data mining algorithms.
K-means, one of the most common clustering algorithms, works by forming k groups from a set of objects based on their similarity. It is not guaranteed that group members will be identical, but group members will be more similar than non-group members. According to standard implementations, k-means is an unsupervised learning algorithm because it learns the cluster without any external input.
The Apriori algorithm learns association rules. Association rules are a type of data mining technique used to discover correlations between variables in a database. Once the association rules have been learned, they are applied to a database with a large number of transactions. Because the Apriori algorithm is used to discover interesting patterns and mutual relationships, it is classified as an unsupervised learning approach. Though the algorithm is highly efficient, it consumes a large amount of memory, occupies a large amount of disc space, and takes a long time.
Expectation-Maximization (EM), like the k-means algorithm for knowledge discovery, is used as a clustering algorithm. The EM algorithm iterates to improve the chances of seeing observed data. Following that, it estimates the statistical model's parameters using unobserved variables, resulting in some observed data. The Expectation-Maximization (EM) algorithm is another example of unsupervised learning because it is used without any labelled class information.
AdaBoost is a boosting algorithm that is used to build a classifier. A classifier is a data mining tool that uses inputs to predict the class of data. The boosting algorithm is an ensemble learning algorithm that runs and combines multiple learning algorithms.
PageRank is widely used by search engines such as Google. It is a link analysis algorithm that determines the relative importance of an object connected to another object in a network of objects. Link analysis is a type of network analysis that investigates the relationships between objects. This algorithm is used by Google Search to understand the backlinks between web pages.
kNN is a classification algorithm that uses a lazy learning algorithm. A lazy learner will do little during the training process other than save the training data. When new unlabeled data is provided as input, lazy learners begin classifying. C4.5, SVN, and Adaboost, on the other hand, are quick learners who begin building the classification model during training. Because kNN is fed a labelled training dataset, it is considered a supervised learning algorithm.
CART is an acronym that stands for classification and regression trees. It is a decision tree learning algorithm that produces regression or classification trees. The decision tree nodes in CART will have two branches exactly. CART is a classifier, just like C4.5. The user-supplied labelled training dataset is used to build the regression or classification tree model. As a result, it is classified as a supervised learning technique.
Although it appears to work efficiently as a single algorithm, Naive Bayes is not a single algorithm. Naive Bayes is a collection of classification algorithms. The family of algorithms assumes that each feature of the data being classified is independent of all other features in the class. To build the tables, Naive Bayes is given a labelled training dataset. As a result, it is classified as a supervised learning algorithm.
Support vector machine (SVM) works similarly to the C4.5 algorithm in terms of tasks, except that SVM does not use any decision trees at all. To classify data into two classes, SVM learns the datasets and defines a hyperplane. A hyperplane is a line equation that looks something like "y = mx + b". To project your data to higher dimensions, SVM exaggerates. SVM defined the best hyperplane to separate the data into the two classes after it was projected.
