Understanding Affinity Propagation: Definition, Explanations, Examples & Code
The Affinity Propagation (AP) algorithm is a type of unsupervised machine learning algorithm used for clustering. It automatically determines the number of clusters and operates by passing messages between pairs of samples until convergence, resulting in a set of exemplars that best represent dataset samples. AP is a powerful tool for clustering and is frequently used in various applications such as image and text seg
Understanding Apriori: Definition, Explanations, Examples & Code
Apriori is an association rule algorithm used for unsupervised learning. It is designed for frequent item set mining and association rule learning over relational databases.
Apriori: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Association Rule
The Apriori algorithm is a widely used method for frequent item set mining and association rule learning over relational databases. It is a type of
Understanding Conditional Decision Trees: Definition, Explanations, Examples & Code
Conditional Decision Trees are a type of decision tree used in supervised and unsupervised learning. They are a tree-like model of decisions, where each node represents a feature, each link (branch) represents a decision rule, and each leaf represents an outcome.
Conditional Decision Trees: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised, Unsupervised
Decision Tree
Conditiona
Understanding Density-Based Spatial Clustering of Applications with Noise: Definition, Explanations, Examples & Code
The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a clustering algorithm used in unsupervised learning. It groups together points that are densely packed (i.e. points with many nearby neighbors) and marks points as outliers if they lie alone in low-density regions. DBSCAN is commonly used in machine learning and artificial intelligence for its ability to
Understanding Eclat: Definition, Explanations, Examples & Code
Eclat is an Association Rule algorithm designed for Unsupervised Learning. It is a fast implementation of the standard level-wise breadth first search strategy for frequent itemset mining.
Eclat: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Association Rule
Eclat is an algorithm used in the field of machine learning and data mining for frequent itemset mining. It is a fast implementation of
Understanding Expectation Maximization: Definition, Explanations, Examples & Code
Expectation Maximization (EM) is a popular statistical technique used for finding maximum likelihood estimates of parameters in probabilistic models. This algorithm is particularly useful in cases where the model depends on unobserved latent variables. EM falls under the clustering category and is commonly used as an unsupervised learning method.
Expectation Maximization: Introduction
Domains
Learning Method
Understanding Hierarchical Clustering: Definition, Explanations, Examples & Code
Hierarchical Clustering is a clustering algorithm that seeks to build a hierarchy of clusters. It is commonly used in unsupervised learning where there is no predefined target variable. This method of cluster analysis groups similar data points into clusters based on their distance from each other. The clusters are then merged together to form larger clusters until all data points are in a single cluster. Hierarchi
Understanding Hopfield Network: Definition, Explanations, Examples & Code
The Hopfield Network is a type of artificial neural network that serves as content-addressable memory systems with binary threshold nodes. As a recurrent neural network, it has the ability to store and retrieve patterns in a non-destructive manner. The learning methods used in Hopfield Network include both supervised and unsupervised learning.
Hopfield Network: Introduction
Domains
Learning Methods
Type
Machine
Understanding Isolation Forest: Definition, Explanations, Examples & Code
Isolation Forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies. It is an ensemble type algorithm, which means it combines multiple models to improve performance.
Isolation Forest: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Ensemble
The Isolation Forest algorithm is an ensemble, unsupervised learning method that has
Understanding k-Means: Definition, Explanations, Examples & Code
The k-Means algorithm is a method of vector quantization that is popular for cluster analysis in data mining. It is a clustering algorithm based on unsupervised learning.
k-Means: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Clustering
Name: k-Means
Definition: A method of vector quantization, that is popular for cluster analysis in data mining.
Type: Clustering
Learning Methods:
* Un
Understanding k-Medians: Definition, Explanations, Examples & Code
The k-Medians algorithm is a clustering technique used in unsupervised learning. It is a partitioning method of cluster analysis that aims to partition n observations into k clusters based on their median values. Unlike k-Means, which uses the mean value of observations, k-Medians uses the median value of observations to define the center of a cluster. This algorithm is useful in situations where the mean value is not a good rep
Understanding k-Nearest Neighbor: Definition, Explanations, Examples & Code
The k-Nearest Neighbor (kNN) algorithm is a simple instance-based algorithm used for both supervised and unsupervised learning. It stores all the available cases and classifies new cases based on a similarity measure. The algorithm is named k-Nearest Neighbor because classification is based on the k-nearest neighbors in the training set. kNN is a type of lazy learning algorithm, meaning that it doesn't have a model to t
Understanding Latent Dirichlet Allocation: Definition, Explanations, Examples & Code
Latent Dirichlet Allocation (LDA) is a Bayesian generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. It is an unsupervised learning algorithm that is used to find latent topics in a document corpus. LDA is widely used in natural language processing and information retrieval to discover the hidden semantic structur
Understanding Multidimensional Scaling: Definition, Explanations, Examples & Code
Multidimensional Scaling (MDS) is a dimensionality reduction technique used in unsupervised learning. It is a means of visualizing the level of similarity of individual cases of a dataset in a low-dimensional space.
Multidimensional Scaling: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Dimensionality Reduction
Multidimensional Scaling (MDS) is a type of dimensionality redu
Understanding Principal Component Analysis: Definition, Explanations, Examples & Code
Principal Component Analysis (PCA) is a type of dimensionality reduction technique in machine learning that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It is an unsupervised learning method commonly used in exploratory data analysis and data compression.
Principal Compo
Understanding Recurrent Neural Network: Definition, Explanations, Examples & Code
The Recurrent Neural Network, also known as RNN, is a type of Deep Learning algorithm. It is characterized by its ability to form directed graph connections between nodes along a sequence, which allows it to exhibit temporal dynamic behavior. RNN has become increasingly popular in recent years due to its ability to handle sequential data of varying lengths. RNN can be trained using both Supervised and Unsupervised
Understanding Sammon Mapping: Definition, Explanations, Examples & Code
Sammon Mapping is a non-linear projection method used in dimensionality reduction. It belongs to the unsupervised learning methods and aims to preserve the structure of the data as much as possible in lower-dimensional spaces.
Sammon Mapping: Introduction
Domains
Learning Methods
Type
Machine Learning
Unsupervised
Dimensionality Reduction
Sammon Mapping is a dimensionality reduction algorithm that belongs to t
Understanding Stacked Auto-Encoders: Definition, Explanations, Examples & Code
Stacked Auto-Encoders is a type of neural network used in Deep Learning. It is made up of multiple layers of sparse autoencoders, with the outputs of each layer connected to the inputs of the next layer. Stacked Auto-Encoders can be trained using unsupervised or semi-supervised learning methods, making it a powerful tool for machine learning engineers to use in their work.
Stacked Auto-Encoders: Introduction
Do