DBSCAN | DBSCAN clustering algorithm implementation

I have a list of Python objects that I want to cluster into an unknown number of groups. The objects can not simply be compared by any distance function proposed by scikit-learn, but rather by a custom defined one. I'm using DBSCAN from the scikit-learn library, which when run on my data raises a TypeError.

Here's what the faulty code looks like. The objects I want to cluster are "Patch" objects, obtained from scanning a 3d mesh :

I am new to using the DBSCAN algorithm.

Quick summary; it has two parameters:

1. epsilon - to specify the acceptable "distance" between two points, under which they can be considered close enough to cluster.
2. minPoints - to specify the minimum number of points that must fall with the distance epsilon to constitute a cluster. If there aren't enough points together, it's just labelled as noise.

I'm using somebody else's DBSCAN algorithm and I have the source code, which I only kind of understand. I was hoping I could use it as-is but then I found some behaviour I wasn't expecting.

I specified a value of 6 for minPoints and yet in my results I got a cluster with only 2 points.

From debugging, I think I can see what's happening. It looks like when the point is examined it has 16 neighbours at a distance below epsilon, so it should qualify as a cluster. Later on, the algorithm sees that 14 of those neighbours had already been assigned to a different cluster, so this cluster ends up with only 2 points in it.

Is minPoints supposed to be strictly enforced?

Is this how a healthy DBSCAN algorithm is supposed to work or is it a bug I need to fix before proceeding?

I am trying to fit OPTICS clustering model to my data using python's sklearn

• I want to cluster big data set (more than 1M records).
• I want to use dbscan or hdbscan algorithms for this clustering task.

When I try to use one of those algorithms, I'm getting memory error.

• Is there a way to fit big data set in parts ? (go with for loop and refit every 1000 records) ?
• If no, is there a better way to cluster big data set, without upgrading the machine memory ?

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)

I am using the R programming language. I created some data and make a KNN graph of this data. Then I performed clustering on this graph. Now, I want to superimpose the clusters on top of the graph.

Here is an example I made up (source: https://michael.hahsler.net/SMU/EMIS8331/material/jpclust.html) - suppose we have a dataset with 3 variables : the longitude of the house, the latitude of the house and the price of the house (we "scale" all these variables since the "price" and the "long/lat" are in different units). We can then make a KNN graph (using R software):

Overview

This code utilises a cluster function that operates on one dimensional arrays and finds the clusters within an array defined by margins to the left and right of every point. I would like to use DBSCAN to replicate this functionality.

Imports:

Say i have the following dataframe stored as a variable called coordinates, where the first few rows look like:

I am using the R programming language.

Using the following code, I am able to put two plots on the same page: