And in fact that intuitive notion of what should be done is exactly what HDBSCAN does. Of course we need to formalise things to make it a concrete algorithm. First we need a different measure than distance to consider the persistence of clusters; instead we will use \lambda = \frac{1}{...
How Does Query Expansion Work in Vector Databases? Query expansion in vector databases enhances search query effectiveness by incorporating additional relevant terms into a query, thus broadening the search's scope for more comprehensive data retrieval. This technique adjusts query vectors to capture a...
that point will be marked as a core-point and included in a cluster, along with all points within the core-distance. A border-point is a point that is within the search distance of a core-point but does not itself have the minimum...
How Does Query Expansion Work in Vector Databases? Query expansion in vector databases enhances search query effectiveness by incorporating additional relevant terms into a query, thus broadening the search's scope for more comprehensive data retrieval. This technique adjusts query vectors to capture a...
The defaultCluster Sensitivityis calculated as the threshold at which adding more clusters does not add additional information, done using the Kullback-Leibler Divergence between the original reachability plot and the smoothed reachability plot obtained after clustering. ...
How Does Query Expansion Work in Vector Databases? Query expansion in vector databases enhances search query effectiveness by incorporating additional relevant terms into a query, thus broadening the search's scope for more comprehensive data retrieval. This technique adjusts query vectors to capture ...
How Does Query Expansion Work in Vector Databases? Query expansion in vector databases enhances search query effectiveness by incorporating additional relevant terms into a query, thus broadening the search's scope for more comprehensive data retrieval. This technique adjusts query vectors to capture a...
Even with our reproducible pipeline, in terms of ARI the Euclidean and hyperbolic metrics have identical performances, higher than the spherical one on three (DBSCAN, OPTICS, and agglomerative clustering) clustering algorithms out of four. Only with HDBSCAN does the Euclidean metric perform worse than...