Top 10 Use Cases of AI in Data Mining Algorithms Explained

In artificial intelligence and machine learning, data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. An algorithm in data mining is a set of heuristics and calculations that creates a model from data. The data mining technique in mined data is used by artificial intelligence systems for creating solutions. Data mining serves as a foundation for artificial intelligence. AI in data mining is a part of programming codes with information and data necessary.

Here is the list of top AI-based data mining algorithms:

C4.5 Algorithm: C4.5 constructs a classifier in the form of a decision tree. These systems take inputs from a collection of cases where each case belongs to one of the small numbers of classes and are described by its values for a fixed set of attributes. A classifier is a tool in data mining that takes a bunch of data representing things we want to classify and attempts to predict which class the new data belongs to. It uses decision trees where the first initial tree is acquired by using a divide and conquer algorithm. The C4.5 is given a set of data representing things that are already classified.

k-means Algorithm: k-means creates k groups from a set of objects so that the members of a group are more similar. It's a popular cluster analysis technique for exploring a dataset. It picks points in multi-dimensional space to represent each of the k clusters. These are called centroids. k-means then finds the center for each of the k clusters based on its cluster members. k-means can be used to pre-cluster a massive dataset followed by more expensive cluster analysis on the sub-clusters.

Expectation-Maximization Algorithm: In data mining, E is generally used as a clustering algorithm for knowledge discovery. EM is simple to implement. And not only can it optimize for model parameters, but it can also guess missing data. This makes it great for clustering and generating a model with parameters. Knowing the clusters and model parameters, it's possible to reason about what the clusters have in common and which cluster new data belongs to.

k-Nearest Neighbors Algorithm: kNN is a classification algorithm. However, it differs from the classifiers previously described because it's a lazy learner. kNN can get very computationally expensive when trying to determine the nearest neighbors on a large dataset. Selecting a good distance metric is crucial to kNN's accuracy.

Naive Bayes Algorithm: This algorithm is based on the Bayes theorem. This is mainly used when the dimensionality of inputs is high. This classifier can easily calculate the next possible output. Each class has a known set of vectors that aim to create a rule that allows the objects to be assigned to classes in the future. This is one of the most comfortable AI algorithms and does not have any complicated parameters. It can be easily applied to massive data sets as well. It does not need any elaborate iterative parameter estimation schemes, and hence unskilled users can understand this.

CART Algorithm: CART stands for classification and regression trees. It is a decision tree learning technique that outputs either classification or regression trees. Scikit-learn implements CART in their decision tree classifier. R's tree package has an implementation of CART. Weka and MATLAB also have implementations.

PageRank Algorithm: PageRank is a link analysis algorithm designed to determine the relative importance of some object linked within a network of objects. The main selling point of PageRank is its robustness due to the difficulty of getting a relevant incoming link. Its trademark is owned by Google.

AdaBoost Algorithm: AdaBoost is a boosting algorithm that constructs a classifier. This algorithm is relatively straightforward to program. It's a super elegant way to auto-tune a classifier since each successive AdaBoost round refines the weights for each of the best learners. All you need to specify is the number of rounds. it's flexible and versatile.

Support vector machines Algorithm: SVMs are mainly used for learning classification, regression, or ranking functions. It is formed based on structural risk minimization and statistical learning theory. It helps in the optimal separation of classes. The main job of SVM is to identify the maximize the margin between the two types. This is a supervised algorithm, and the data set is used first to let SVM know about all the classes.

Apriori Algorithm: This is widely used to find the frequent itemsets from a transaction data set and derive association rules. Once we get the frequent itemsets, it is clear to generate association rules for larger or equal specified minimum confidence. Apriori is an algorithm that helps in finding routine data sets by making use of candidate generation. After the introduction of Apriori data mining research has been specifically boosted. It is simple and easy to implement.