Independence

NXG Logic's mission is, in part, to empower users to become less dependent on time and legacy programs which prevent you from maximizing your productivity. Our belief is that since time is the greatest commodity, a majority of software design and implementation should focus on novel approaches for decreasing the amount of time required to complete routine data analytic tasks.

By using the Enterprise edition of NXG Explorer, you will be able to:

Rapidly perform text mining of linguistic data
Perform non-linear manifold learning, which involves unsupervised dimensional reduction techniques
Apply machine learning methods for unsupervised class discovery and supervised class prediction
Rapidly employ model building strategies for linear, logistic, and Cox proportional hazards regression models
Rapidly analyze a set of data to inform about distributional tendencies, summary statistics, and hypothesis testing using parallel feature analysis (PFA)
Cluster analyze a set of data
Rapidly publish results and create presentation materials

To this end, one of the ultimate goals of NXG Logic is to enable users to increase portability and minimize dependency on time, other analysts, servers, and usage of command line code as a means for increasing individual productivity, not just team productivity. As an example, the greatest realization for achieving independence would occur when traveling to a conference or meeting and having limited time to perform a new analysis for which you have no experience and have no developed command line code.

The machine learning approaches available in Explorer include:

Crisp K-means cluster (class discovery module)
Fuzzy-K-means cluster (class discovery module)
Unsupervised neural gas (class discovery module)
Gaussian mixture models (class discovery module)
Unsupervised random forests (class discovery module)
Kernel-based PCA (class discovery module)
Kernel Gaussian radial basis function PCA (class discovery module)
Kernel Tanimoto distance-based PCA (class discovery module)
Diffusion maps (class discovery module)
Localized linear embeddings (class discovery module)
Laplacian eigenmaps (class discovery module)
Locally preserved projections (class discovery module)
Stochastic neighbor embedding (class discovery module)
Sammon mapping (class discovery module)
Decision tree classification (class prediction module)
Supervised random forests (class prediction module)
K-nearest neighbor (class prediction module)
Learning vector quantization (class prediction module)
Support vector machines (class prediction module)
Kernel regression (class prediction module)
Supervised neural gas (class prediction module)
Mixture of experts (class prediction module)

With regard to AI, there are plenty of artificial intelligence methods incorporated into Explorer, and these include:

Kohonen networks, or self-organizing maps (class discovery module)
Unsupervised artificial neural networks (class discovery module)
Supervised artificial neural networks (class prediction module)
Swarm intelligence (class prediction module)

Both the class discovery and class prediction modules enable users to run all the methods simultaneously on a dataset, so that results can be compared with one another. As you can see, the capabilities of Explorer can enable you to get up to speed quite rapidly, so that you can pursue new analytic approaches to tackling challenges brought about by complex data.

Once you begin expanding your analytic experience with Explorer, you will be able to establish new leads in the machine learning and AI fields. The text mining capabilities in Explorer will enable you to become more familiar with information retrieval technologies that are employed in linguistics, and can spark creativity for performing new analyses which heretofore have not been done with text data. The machine learning capabilities of Explorer will enable you to start envisioning data differently, so you can conquer new analytic approaches. In either case, use of the Explorer platform will allow you rapidly gain experience and rapidly thrust you forward into new areas of data analytics, so you can create new collaborations from these experiences.