A classification function assigns data items to discrete categories. For example, a classification function whose input is pictures of pets might sort them into dogs, cats, rabbits and so on.
Unsupervised classification where the classes are neither labelled nor known beforehand is known as clustering. It was decided to subsume clustering under classification rather than capturing it as a separate functional building block because there is no clear demarkation between the two.
Classification is captured both as a functional building block and as a use case as it has both functional and business aspects.
- used by
- ALG_Adaptive resonance theory network ALG_Autoencoder ALG_Averaged one-dependence estimators ALG_Bayesian network ALG_Convolutional neural network ALG_DBSCAN ALG_Decision tree ALG_Discriminant analysis ALG_Expectation maximization ALG_Hierarchical clustering ALG_Hopfield network ALG_Local outlier factor ALG_Logistic regression ALG_Long short-term memory network ALG_Markov random field ALG_Naive Bayesian Classifier ALG_Nearest Neighbour ALG_One Rule ALG_Perceptron ALG_Probabilistic latent semantic indexing ALG_Radial basis function network ALG_Random forest ALG_Spherical k-means ALG_Support vector machine ALG_Zero Rule ALG_k-means ALG_k-medians ALG_k-medoids SPT_Bagging SPT_Boosting SPT_Elastic net SPT_Evolutionary selection SPT_LASSO SPT_Locally weighted learning SPT_Ridge regression SPT_Stacking