A machine learning algorithm is a set of rules or processes used by an AI system to conduct tasks.
One of the most commonly used centroid-based clustering techniques is the k-means clustering algorithm. K-means assumes that the center of each cluster defines the cluster using a distance measure, mostly commonly Euclidean distance, to the centroid. To initialize the clustering, you provide a num...
Grid-based clustering algorithms divide the data space into a finite number of cells or grid boxes and assign data points to these cells. The resulting grid structure forms the basis for identifying clusters. An example of a grid-based algorithm is STING (Statistical Information Grid). Grid-base...
as well as dimensionality reduction methods like principal component analysis (PCA). Its primary goal is to discover hidden or in-built structures within the dataset, such as grouping data that are similar to each other(clustering) or reducing the attributes or columns of the...
Unlike PCA, t-SNE is a non-linear technique that preserves the local structure of the data. It is especially suitable for the visualization of high-dimensional datasets. By reducing the dimensionality of data, these techniques help in mitigating the curse of dimensionality, improving the ...
Using PCA, we projected the data to a 2-dimensional space: This is very helpful for presenting data to various people in your organization. Moreover, it makes it possible to visualize and inspect clustering and classification algorithms and their performance. ...
Principal component analysis (PCA), in which the computer analyzes a data set and summarizes it so that it can be used to make accurate predictions. Withsemi-supervised learning, the computer is provided with a set of partially labeled data and performs its task using the labeled data to unde...
(PCA) of the functional profiles of major orders over time. The functional profile of an order is the gene abundances of core families in this order in every KEGG category.fPCA of the taxonomic profiles of KEGG categories over time. The taxonomic profile of a KEGG category is the gene ...
As per the Kaiser’s rule, to select the number of components while performing PCA (Principal Component Analysis), the components having eigenvalue of more than 1 are chosen. In our study components (Keywords) with id number 1 to 9 have eigenvalue greater than 1 and the keywords with id ...
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit,