Anomaly Detection
One Class SVM
One-Class SVM Transformation Description One-Class Support Vector Machine (One Class SVM) is an unsupervised variation of SVM used for anomaly detection. One-Class SVM is an unsupervised algorithm for outlier detection. It detects whether a new data ...
Local Outlier Factor
Local Outlier Factor Description The Local Outlier Factor (LOF) algorithm is an unsupervised machine learning algorithm based on the concept of local density. It compares the density of data points in the distribution to the density of the ...
Isolation Forest
Isolation Forest Description Isolation Forest is an unsupervised algorithm used for anomaly detection that isolates the anomalies rather than building a model of normal instances. Why to use Isolation forest detects anomalies faster and requires less ...
DBSCAN
DBSCAN Description DBSCAN stands for Density Based Spatial Clustering of Applications with Noise. It is an unsupervised ML algorithm used for segregating high-density clusters from those having low density. Why to use To create data point clusters ...
Anomaly Detection
Anomaly detection is the discovery or classification of events or observations that differ substantially from most of the data. Anomalies are also known as outliers, deviations, novelties, exceptions, or noise. Anomaly detection is categorized into ...