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Reposting because i didn't add in my data earlier;

I have been running a DBSCAN algorithm on R for a project where I'm creating clusters of individuals based on the location they are in.

I need clusters of 3 people (k=3), each matched on the location of each individual and my eps value is 1.2

The problem I'm facing with my algorithm is that the cohorts/clusters are of variable size.

This is my output after running the clustering code, and as you can see, there are 5 variables in cluster #2, and I want to split this up into 3 + 2 (so, cluster number 3 will have 3 points and cluster number 4 will have 2 points)

We are using forge viewer(v7) in our web application.

Our requirement is to crop particular room/area from the forge viewer. For example, if we have shown a house model in the forge viewer then if a user select a kitchen(from menu or navbar) then the viewer should show only kitchen area (including all its objects like cabinets, burner, fridge, sink etc.) and all other objects/sections should be hidden. Similarly for bedrooms, baths etc. It will be just for viewing purpose at run time and not for any automation.

We are getting room coordinates(min and max X, Y, Z) with the help of following using forge API(with Revit engine).

I want to do something like

I'm attempting to run DBSCAN against some grouped coordinates in order to get sub-clusters. I've clustered some spacial data and I'd now like to further divide these regions according to the density of points within them. I think DBSCAN is probably the best way to do this.

My issue is that I can't figure out how to run DBSCAN against each cluster seperately and then output the cluster assignment as a new column. Here's some sample data:

I have completed and plotted the DBSCAN cluster in R markdown.

This is my code currently:

ORB doesn't find keypoints near the edge of an image and I don't understand why. It seems worse that SIFT and SURF and I would expect the opposite.

If I understand correctly then SIFT/SURF use a 16x16 and 20x20 square block respectedly around the test-point so I would expect them not to find keypoints 8 and 10 pixels from an edge. FAST/ORB uses a circle of diameter 7 around the test-point so I expected it to find keypoints even closer to the edge, perhaps as close as 4 pixels (though I think the associated algorithm, BRIEF, to describe keypoints uses a larger window so this would remove some keypoints).

An experiment makes nonsense of my prediction. The minimum distance from the edge in my experiments vary with the size and spacing of the squares but examples are

• SIFT .. 5 pixels
• SURF .. 15 pixels
• ORB .. 39 pixels

Can anyone explain why?

The code I used is below. I drew a grid of squares and applied a Gaussian blur. I expected the algorithms to latch onto the corners but they found the centres of the squares and some artifacts.

Question: The best way to find out the Eps and MinPts parameters for DBSCAN algorithm?

Problem: The goal is to find the locations (clusters) based on coordinates (input data). The algorithm calculates the most visited areas and retrieves these clusters.

Approach:

I defined the epsilon (EPS) parameter as 1.5 km - converted to radians to be used by the DBSCAN algorithm: epsilon = 1.5 / 6371.0088 (ref to this 1.5 km: https://geoffboeing.com/2014/08/clustering-to-reduce-spatial-data-set-size/).

If I define the MinPts to a low value (e.g. MinPts = 5, it will produce 2000 clusters), the DBSCAN will produce too many clusters and I want to limit the relevance/size of the clusters to an acceptable value. I use the haversine metric and ball tree algorithm to calculate great-circle distances between points.

Suggestions:

1. knn approach to find EPS;
2. domain knowledge and to decide the best values for EPS and MinPts.

Data: I'm using 160k coordinates but the program should be capable to handle different data inputs.

I am trying to perform hyperparameter tuning for Spatio-Temporal K-Means clustering by using it in a pipeline with a Decision Tree classifier. The idea is to use K-Means clustering algorithm to generate cluster-distance space matrix and clustered labels which will be then passed to Decision Tree classifier. For hyperparameter tuning, just use parameters for K-Means algorithm.

I am using Python 3.8 and sklearn 0.22.

The data I am interested is having 3 columns/attributes: 'time', 'x' and 'y' (x and y are spatial coordinates).

The code is:

I am trying to perform hyperparameter tuning for Spatio-Temporal K-Means clustering by using it in a pipeline with a Decision Tree classifier. The idea is to use K-Means clustering algorithm to generate cluster-distance space matrix and clustered labels which will be then passed to Decision Tree classifier. For hyperparameter tuning, just use parameters for K-Means algorithm.

I am using Python 3.8 and sklearn 0.22.

The data I am interested is having 3 columns/attributes: 'time', 'x' and 'y' (x and y are spatial coordinates).

The code is: