Data mining DBSCAN task
Budget: $30 – $250 CAD
To illustrate the practical application of automatic
classification with DBSCAN. First, we will observe the behavior of the algorithm in a
synthetic case involving k-means failure. We will see how to use classical heuristics to
determine the parameters of the algorithm and how to interpret the partitioning obtained.
Finally, we will apply DBSCAN on a real data set and we will illustrate its use for the
detection of outliers.
classification with DBSCAN. First, we will observe the behavior of the algorithm in a
synthetic case involving k-means failure. We will see how to use classical heuristics to
determine the parameters of the algorithm and how to interpret the partitioning obtained.
Finally, we will apply DBSCAN on a real data set and we will illustrate its use for the
detection of outliers.