Find unknown structures in large and diverse data sets

DATA MINING

Data mining is used to identify unknown structures in large amounts of data and is part of exploratory data analysis. Methodically, data mining consists of clustering and dimension reduction.

 

The DATANOMIQ team relies on a constantly growing portfolio of data mining algorithms. Typical use cases are the preparation of data for the application of predictive analytics or the visualisation of multi-dimensional data by projection into a 2- or 3-dimensional space. With DQ Data Mining you get deeper insights into your data than ever before thanks to modern visualisation methods and clustering procedures.

CLUSTERING

Clustering to form classes of business events such as orders, financial transactions or machine states

DIMENSIONAL REDUCTION

Dimension reduction for Feature Engineering before using Predictive Analytics

ANOMALY DETECTION

Recognition of atypical process patterns in financial transactions or in machine parameters using automatic encoders (Deep Learning)