The Bureau of Labor Statistics is cutting back its collection of data on consumer prices, raising questions about the reliability of federal economic statistics under President Trump. Every month ...

When analyzing heterogeneous data comprising numerical and categorical attributes, it is common to treat the different data types separately or transform the categorical attributes to numerical ones.

This article presents a technique for clustering mixed categorical and numeric data using standard k-means clustering implemented using the C# language. Briefly, the source mixed data is preprocessed ...

Clustering analysis, as an important technique in data mining, aims to identify the nature groups or clusters of data objects in the attribute space. Data objects in real-world applications are ...

The other alternative is turning categorical data into numeric values using one of several encoding techniques. These techniques all tend to be slow and produce poor results – even making some goals ...

Encoding categorical data is a process of converting categorical data into integer format so that the data with converted categorical values can be provided to the different models.

Dealing with categorical features is a common thing to preprocess before building machine learning models.There are a variety of techniques to handle categorical data which I will be discussing in ...