In machine learning, data can be categorized into numeric and non-numeric types. Non-numeric data typically includes text, categorical, or qualitative features that are not directly usable by machine ...

This repository introduces two novel CP approaches for mining closed interval patterns directly from numerical datasets. Unlike existing methods that require pre- and post-processing steps to handle ...

Abstract: Input-discriminative local differential privacy (ID-LDP) protects user data with a different range of values, which improves the utility of the estimated data compared to traditional LDP.

As sharing of research data becomes more important, we are facilitating this by integrating the Figshare repository into our submission system. Two publications have called for the redefinition of ...

Gathering and entering numbers on a computer is one of the most laborious aspects of data management. Ten-key data entry expedites this process, focusing only on the number keys, 0 through 9, which ...

contains only numbers, and sometimes a decimal point and/or minus sign. When they are read into a SAS data set, numeric values are stored in the floating-point format native to the operating ...

Clustering non-numeric -- or categorial -- data is surprisingly difficult, but it's explained here by resident data scientist Dr. James McCaffrey of Microsoft Research, who provides all the code you ...