Data Mining

Girvan–Newman Algorithm

What is the Girvan–Newman Algorithm? The Girvan–Newman (GN) algorithm is a classic community detection method for networks (graphs). It identifies clusters (communities) by iteratively removing edges that act as “bridges”…

Linear Regression

What is the Linear Regression Algorithm? Linear regression is a supervised learning algorithm that models the relationship between one or more input variables (features) and a continuous output variable (target)…

Apriori Algorithm in Data Mining: Working, Applications & Association Rule Learning

What is Apriori Algorithm? Large transactional databases can be mined for frequent itemsets and association rules using the traditional Apriori approach. Finding patterns of items that commonly co-occur in transactions…

FP-Growth Algorithm in Data Mining: Fast Frequent Pattern Mining Explained

What is FP-Growth (Frequent Pattern Growth)? An effective and scalable technique for mining frequent itemsets in big transactional databases is the FP-Growth (Frequent Pattern Growth) algorithm. By employing a tree-based…

K-Nearest Neighbors (KNN) Algorithm: Classification, Working & Real-World Applications

What is k-Nearest Neighbors (k-NN)? A supervised learning approach for classification and regression problems is called k-Nearest Neighbors (k-NN). Since it is instance-based and non-parametric, it does not explicitly learn…

Naive Bayes Algorithm: Working Principle, Types & Applications in Machine Learning

What is Naïve Bayes Algorithm? Naïve Bayes is a probabilistic classification algorithm that relies on Bayes’ Theorem. It assumes that all features in the dataset are independent of one another.…

Support Vector Machine (SVM) Algorithm: Concept, Working, and Applications in ML

What is SVM? A supervised learning approach for classification and regression problems is called Support Vector Machine (SVM). Pattern recognition, text categorization, and bioinformatics all make extensive use of it,…

Data Mining Concepts, Techniques and Applications

Introduction of Data Mining Organizations are producing enormous volumes of data from a variety of sources in today’s data-driven environment, from social media interactions to IoT devices and transactional databases.…