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Table 5 Data Analytics Algorithms

From: Big data analytics in smart grids: a review

Category Algorithm description
Supervised Learning Decision tree A non-parametric method with a tree-like method whose leaves represent class labels and branches represent conjunctions of features
Naive Bayes A probabilistic method based on Bayes theorem with the assumption of independence between every pair of features
Support vector machine classifier An algorithm to find a separating hyperplane between the two classes by mapping the labelled data to a high-dimensional feature space
K Nearest Neighbor A non-parametric method based on the minimum dissimilarity between new items and the labelled items in different classes
Random Forest An algorithm consisting of a collection of simple tree predictors independently for the estimation of the final outcome
Unsupervised Learning K-means An unsupervised learning method with a given number of clusters to sort the data based on the average value of data in each group as the centroid
K-medoids An unsupervised learning method similar to k-means by assigning the centroid of each group with an existing data point instead of the average value
Hierarchical Clustering An alternative approach which aims to build a hierarchy of clusters in a dendrogram without a given number of clusters
DBSCAN A density-based clustering algorithm to identify clusters with specific shape in distribution
Expectation-Maximization An iterative way to approximate the maximum likelihood estimates for model parameters
Correlation FP-Growth Algorithm An efficient method for mining the complete set of frequent patterns with a special data structure named frequent-pattern tree with all the association information reserved
Apriori Algorithm A classical data analytics algorithm to discover the potential association rules among frequent items
Dimensionality reduction Principal Component Analysis An orthogonal transformation of data with a new coordinate system with the greatest variance projected to the first coordinate
Self-organizing Map A type of artificial neural network for a low-dimensional representation of the training data space
Random Matrix An algorithm which reveal potential regulations with high order matrices for massive data by eigenvalue analysis