After running the DBSCAN algorithm, I went on to create OPTICS, which is an extension of it.
preprocessed the data by transforming the object values into numerical ones and normalizing/standardizing the data.
Determined the distance between each location using the Haversine distance formula.
The OPTICS algorithm was subsequently implemented as follows: optics = OPTICS(min_samples=5, metric=’precomputed’)optics.fit (matrix of haversine)
collected cluster information, including reachability distances and core samples, and identified the noise sites.
In order to comprehend the spatial distribution, the clusters were also visualized on the map.
