Sentence examples for data exploration methods from inspiring English sources

However, of the more than 20 kinds of data exploration methods evaluated, the vast majority is neither scientific nor heuristic.

All these data exploration methods together allow evaluation of the buildings isolation from the outdoor environment, as well as enhancing internal gradients; further spontaneous values, not normal behaviour and sudden changes, which are the most dangerous events for a cultural heritage site, can be evaluated.

Finally all these data exploration methods together allow evaluation of the building's isolation from the outdoor environment, as well as enhancing internal gradients; further spontaneous values, not normal behaviour and sudden changes, which are the most dangerous events for a cultural heritage site, can be evaluated.

This example demonstrates that availability of appropriate annotations directly affects the effectiveness of data exploration methods.

We show results of an experimental evaluation of the effectiveness of our data exploration methods, demonstrating that annotation-based similarity, as well as clustering and query-by-example based on this similarity, help users find experiments of interest.

Adapted Likelihood-Based Topology Tests and Data Exploration Methods One of the most popular likelihood-based methods for assessing incongruence is an adaptation of the Shimodaira-Hasegawa (SH; Shimodaira and Hasegawa 1999 ) or AU (Shimodaira 2002 ) topology tests.

P values from AU or SH tests, as well as raw tree likelihoods, have also been used in data set exploration methods.

In general, the feature of censoring means that special methods of analysis are needed, and standard graphical methods of data exploration and presentation, notably scatter diagrams, cannot be used.

The methods of data exploration have become the centerpiece of phylogenetic inference, but without the scientific importance of those methods having been identified.

The robustness of our research findings were demonstrated by linking a number of spatial data explorations and statistical methods with GIS.

To process the data, the following statistical methods were used: data exploration analysis, descriptive statistics and correlation analysis.