Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended...

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves...

In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying...

order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems...

Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent...

Unsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic map Information bottleneck method Association rule learning algorithms...

Anomaly/outlier/change detection Association rule learning Bayesian networks Classification Cluster analysis Decision trees Ensemble learning Factor analysis Genetic...

Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic...

Contrast set learning is a form of association rule learning that seeks to identify meaningful differences between separate groups by reverse-engineering...

tools are machine learning libraries for Python, like scikit-learn. Some major rule induction paradigms are: Association rule learning algorithms (e.g....

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

reduce the search space for the problem. The support metric in the association rule learning algorithm is defined as the frequency of the antecedent or consequent...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

processing algorithms and itemset mining which is typically based on association rule learning. Local process models extend sequential pattern mining to more...

recommendation systems For the most part, FP discovery can be done using association rule learning with particular algorithms Eclat, FP-growth and the Apriori algorithm...

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related...

network Relational Markov network Relational Kalman filtering Association rule learning Formal concept analysis Fuzzy logic Grammar induction Knowledge...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

Eclat Textile, a Taiwanese textile company Lotus Eclat, a car Association rule learning § Eclat algorithm, an algorithm This disambiguation page lists...

of learning language and communication, and the stage where a child begins to understand rules and symbols. This has led to a view that learning in organisms...

to the frequent pattern discovery approach that underlies most association rule learning techniques. Frequent pattern discovery techniques find all patterns...

Regression Models Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines Apriori Association Rules Learning k-Means Clustering Model Performance...

In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

p(x|y)p(y)} by Bayes' rule. Semi-supervised learning with generative models can be viewed either as an extension of supervised learning (classification plus...

from are based on a consistent and simple rule. Both offline data collection models, where the model is learning by interacting with a static dataset and...

regression, factor analysis, clustering, classification and association rule learning. Tanagra is an academic project. It is widely used in French-speaking...

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update...

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration...