. . . .
. . . .
your data
Clustering is a method for grouping similar data together
Imagine you have a large amount of information (such as customers or products), and you
want to divide it into groups where the elements in each group are more similar to each other than
to those in the other groups. Clustering algorithms use unsupervised learning, i.e. the algorithm
groups data into clusters according to their similarities without having any prior knowledge of
how the data is classified, in other words, without it having been "tagged". This makes it
possible to analyze the structure of the data and discover groups of data inaccessible to human
analysis.
Quantum computer
will process it with Q-Medoids
Classical clustering methods are widely used and effective in many contexts, but they have their
limitations.
In particular, they are computationally very expensive, as the algorithms can become very
slow or inefficient when processing very large data sets. These algorithms are also sensitive to
outliers, which can lead to incorrect groupings.
your clustered data
Our Q-Medoids quantum module uses the K-MEDOIDS algorithm, a specific clustering technique.
Unlike other more common methods, such as K-means, which use barycenters to define group
representatives, K-medoids identifies actual elements of the dataset as representatives.
This
makes it much easier to interpret the groups produced. In addition, it improves the robustness of
the results and can yield more relevant results, especially when the data have outliers or
noise.
Q-MEDOIDS
Drastically accelerate your clustering, without quality degradation, access an unlimited number of
clusters, guarantee robustness to extreme values, and facilitate interpretation of results.
With Q-MEDOIDS, you can analyze customer groups, find anomalies, segment images, plan locations and optimize the
supply chain.
