TDA: Statistical Tools for Topological Data Analysis

Tools for Statistical Analysis of Persistent Homology and Density Clustering For this purpose, this package is an efficient algorithm for the R interface for "GUDHI" in the C ++ library, "Dionysus" and "PHAT" . This package is described in Fas et al. Also implements the method. (2014) and Chazal et al. (2014) statistical significance for analyzing permanent homology

Ayasdi is one of my personal favorites! The secret behind Ayasdi technology is the topology. Topology is the field of mathematics to study shape. Topology data analysis (TDA) means that the field is suitable for analyzing large and very complex data. It is based on the insight that all data has a latent form and its shape has meaning. MetaMind: MetaMind wants everyone to learn deeply. It includes a set of techniques that does not require domain experts to program expertise into algorithms. MetaMind improves and distributes state-of-the-art solutions for natural language processing, computer vision, and database prediction.

One method, Topological Data Analysis (TDA), excels in having hidden relationships in performance data and identifying meaningful relationships without asking specific questions about the data. As a result, an output that can express complex phenomena is obtained, so we can show weaker signals and stronger signals. Because of this pillar, most companies use a standard set of supervised machine learning algorithms, including random forests, gradient enhancement, and linear / sparse learners. However, unsupervised work from the previous procedure is very helpful in many ways. For example, you can generate a related function to predict tasks and find fragments of local data (system errors) that may be encountered by monitored algorithms.