Questions tagged [dbscan]

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm. DBSCAN views clusters as areas of high density separated by areas of low density.

Filter by
Sorted by
Tagged with
0
votes
0answers
15 views

Good clustering method for a dataset with duplicate records

I want to use a clustering algorithm for a data set that each row/record has 3 features/columns. All the features are integers. First, I have tried DBSCAN method because it is a method that I know ...
0
votes
0answers
4 views

Compare ground truth images of classes with clustering output classes images

I have a ground truth input of 24 folders (classes) and each folder has 20 images. Each image has an encoding feature template and consists of 512 dimensions like ...
1
vote
0answers
17 views

How to achieve good clustering results in subsequence time series clustering with DBSCAN?

I want to find patterns in a time series and use clustering for that. Before I cluster, I create subsequences from the time series using a sliding window approach. (STS-clustering) So far I have tried ...
1
vote
2answers
31 views

Separate clusters from a single column of data

Is there any statistical method to separate the visual groups of data from the graph shown below? As you can see from the figure, there are 4 clusters of data which I wan to separate them out. I ...
0
votes
0answers
16 views

Clustering Highly Skewed and Segmented Data

So I am trying to cluster a dataset that looks like the following: I have tried K-Means and GMM, which give me horrible results. I have tried DBSCAN, which was okay, but it is difficult to choose the ...
0
votes
1answer
15 views

Generated more than 10+ clusters DBScan

So I am trying to generate a clustered graph using DBScan. I am able to do so but somehow gotten the clustered graph with more than 10+ clusters. It can be seen below. What I was expecting was ...
0
votes
0answers
23 views

Which is the best clustering algorithm for clustering multidimensional data with low density difference?

I am working on a project currently and I wish to cluster multi-dimensional data. I tried K-Means clustering and DBSCAN clustering, both being completely different algorithms. The K-Means model ...
1
vote
1answer
29 views

Unsupervised soft clustering methods

I have a D-dimensional dataset composed of exactly two clusters (this is known) for which I have no labels; the clusters can potentially be wildly imbalanced. I'm after a soft (or fuzzy) clustering ...
0
votes
1answer
35 views

Searching for a DBSCAN toy example

I have been searching in many sources (data science books, google scholar, google and medium) but I havn't found a good example. I'm searching for something like that just in DBSCAN. Thanks
0
votes
0answers
173 views

Visualizing DBSCAN over successive iterations

I stumbled across Naftali Harris' Visualizing DBSCAN Clustering site that creates a movie as you watch DBSCAN work on various synthetic datasets. Are there any python visualization libraries that ...
0
votes
0answers
21 views

Understanding Xi influence on forming clusters in the OPTICS algorithm

Setting: I want to cluster univariate time series of equal length with the OPTICS algorithm and extract clusters. Each time series has 24 measurements. Similarity measure that I use is the ...
1
vote
0answers
34 views

How to plot categories after clustering

I am trying to plot the categories I have obtained via DBSCAN on a 30-dimensional dataset 12 categories, and I want to visualize them in a 2d plot. My procedure was to reduce that 30-dimensional ...
0
votes
1answer
131 views

which metrics are suitable for density-based clustering validation?

I'm working on a project where I use several clustering methods, mainly density based ones such as hdbscan, optics... I'm looking for a metric to evaluate clustering results that takes into account ...
1
vote
0answers
408 views

How to use Gower's Distance with DBSCAN algorithm in Python

I have been researching about using DBSCAN with sklearn in python but it doesn't have Gower's distance metric built in. All the other implementations are in R in this community. I'm using a dataset ...
0
votes
0answers
47 views

Clustering before/after t-SNE?

My dataset contains 100 features about travel behaviors of ~500 users, created using doc2vec. I want to create clusters of these users and I'm planning to use either k-Means or DBSCAN algorithm. My ...
0
votes
0answers
126 views

Understanding the kdist graph used to select DBSCAN epsilon parameter

I need to use DBSCAN for my research and am having trouble understanding the kdist graph used to select the epsilon parameter - specifically, I do not understand what is happening behind the scenes ...
0
votes
0answers
38 views

Is there a way to use HDBSCAN to fit on batches?

I would prefer to use a DBSCAN like algorithm that supports batching data. I can use minibatch k-means but that would require a lot of additional work for optimal performance that HDBSCAN takes care ...
1
vote
1answer
30 views

Correct clustering approach for segmenting stores

Domain : Retail I have a set of stores which I want to cluster into similar stores based on 10 variables: revenue, avg income, market share etc. I took 2 approach: Approach 1: Given there are 10 ...
0
votes
1answer
163 views

Suggestions to cluster more than 300k observations

I am trying to perform an hierarchical clustering on data frame that contains 300k records 7 features (3 binaries and 4 continuous) in order to get insights on what looks like my dataset. I've chosen ...
1
vote
1answer
51 views

How does non-uniqueness of data (aka duplicate data points) affect clustering?

I am trying to self-learn more about different clustering methods. I think I understand the main idea of the algorithms, but perhaps their use-cases can shed light on something that puzzles me - ...
1
vote
1answer
353 views

Weighted Minkowski distance for DBSCAN

I'm trying to make clustering of image's pixels with DBSCAN, using RGB values and pixel's coordinates as features. It works well with just RGB values as features, but I want pixels with the same color,...
3
votes
1answer
253 views

Clustering data with covariance for each point

I am looking to cluster data points that each have a covariance around itself (based on some function of its neighbourhood, but how I got it is not important). I would like to use the covariance to ...
1
vote
2answers
2k views

Determining epsilon for DBSCAN

I'm using the method described in this paper for determining the optimal epsilon value for DBSCAN clustering in which a plot of the nearest neighbors is used: However, the plots in the paper and ...
-1
votes
2answers
43 views

Unsupervised Clustering

My research is about comparing K-means and DBSCAN, and Im using unsupervised learning method in clustering. Is it true that the number of cluster in K-means is also the same number as the unique ...
0
votes
0answers
34 views

Is there a standard way to incorporate target continuous variable in DBSCAN, and use the model for inference?

I'm trying to use a density based clustering method on this data, where Y is the target variable Y and X is the independent variable (both are continuous): Snip 1 is hex-bin-plot of X and Y: So i am ...
0
votes
1answer
218 views

Issue in evaluating the performance of my “clustering algorithm” using NMI, ARI when the “ground truth” is available? [duplicate]

(**Edited the question after the initial comments) Suppose, Ground_truth_data = [1, 1, 1, 1, 1, 1, 1]; Clustering_result = [1, 1, 1, 1, 1, 1, 2]; Here, as you can see, there are "7" instances of ...
0
votes
1answer
80 views

clustering location based on sorted time

I clustered my dataset based on location using DBSCAN(haversine). Everything is OK until this. However, I'd like to use the time series while I'm clustering my dataset. For example. You were at home ...
0
votes
1answer
915 views

Noise and Outliers in DBSCAN

Why are noise and outliers treated as the same concept in DBSCAN (density-based spatial clustering of applications with noise)?
3
votes
1answer
3k views

Anomaly detection in multivariate time series data

I am trying to solve an anomaly detection problem that consists of three variables captured over a span of five years. It is an unsupervised problem, and I believe density-based clustering methods ...
2
votes
2answers
818 views

Trajectory clustering - preprocessing and algorithms

Context Consider the following problem where we have two time dependent (yearly) measures: Fertility rate Life expectancy And a dimension: country. In other words we have over two hundred "...
3
votes
1answer
2k views

Principal component analysis and DBScan

My data has 30 dimensions and 150 observations. I want to cluster the data with DBScan. Is there a difference between: 1. Performing a PCA and clustering all 30 principal components or 2. Just ...
1
vote
2answers
1k views

What is a good clustering fitness metric for DBSCAN?

Usually, my go-to goodness of fit for evaluating clustering (e.g., k-means) is the average silhouette. However, for DBSCAN it doesn't work since there are lots of non-clustered points. So ...
7
votes
2answers
3k views

Does k-means have any advantages over HDBSCAN expect for runtime?

I have recently learned about HDBSCAN (a fairly new method for clustering, not yet available in scikit-learn) and am really surprised at how good it is. The following picture illustrates that the ...
-1
votes
1answer
28 views

Clustering of spatial data with maximum extension

I am using DBSCAN to cluster my spatial geolocated data. However, some of the clusters that I find are really large, extending kilometers, which makes no sense for my application. The approach I am ...
1
vote
1answer
185 views

Intrinsic dimensionality and density-based clustering

I’ve got several thousand observations in 350-dimensional space, in a relatively sparse matrix (median observation has 11 non-zero dimensions). I'm using a density-based clustering algorithm, DBSCAN, ...
0
votes
1answer
2k views

Does it make sense to run DBSCAN on the output from t-SNE? [duplicate]

Performed time series clustering where I used DTW to generate a distance matrix. The distance matrix was then given as an input to t-SNE where the two-dimensional results from t-SNE were used for ...
0
votes
1answer
896 views

Identifying clusters with OPTICS in R

I am experimenting with OPTICS clustering in R and from what I have seen in the vignette the valleys and peaks somehow determine the number of clusters which than can be extracted using ...
0
votes
1answer
422 views

DBSCAN input format? [closed]

just trying to understand the process of removing outliers in my data using the python Scikit's DBSCAN function. As an example, given aDataFrame of data, which includes both my target and features, I ...
-1
votes
1answer
71 views

I have this 3 clustering algorithms and I want to figure out which algorithm has the best algorithm for clustering

I'm new with clustering. I have this 3 algorithms and I want to figure out which algorithm has the best algorithm for clustering. I posted an image below, to show my clusters. I am confused on how to ...
4
votes
1answer
535 views

Is (a) multicollinearity and/or (b) binary variables an issue for DBSCAN? if so, how can one correct for these issues?

I have read some related questions, such as: Why are mixed data a problem for euclidean-based clustering algorithms?, What data structure to use for my cluster analysis or what cluster analysis to use ...
1
vote
1answer
2k views

Scikit-Learn's DBSCAN Unable to Cluster Toy Data Sets [closed]

I am attempting to demonstrate how DBSCAN can cluster data of arbitrary 2D shapes. I've created two toy datasets in Scikit-Learn using the make_blobs and ...
0
votes
1answer
931 views

DBSCAN considers all data points noise for reduced time series data [closed]

I had a data matrix 609 rows × 264 columns, time-series data. Data was reduced using t-SNE algorithm to 3 dimensions. When being clustered I get zero clusters, where all data points are considered ...
2
votes
1answer
2k views

What is the elbow point in this plot and how to compute it?

I would like to find automatically a reasonable elbow point in this plot. In particular to select the value of epsilon in DBSCAN. The points are sorted on descending value of ordinate. Visually I ...
0
votes
1answer
303 views

Persistent Cluster ID's for DBSCAN

When executing the DBSCAN algorithm over multiple runs on similar data (but not the same), I would like to generate persistent ID's so we can monitor how the clusters changed over time. Selection of ...
3
votes
2answers
2k views

Why is DBSCAN deterministic?

Recently, I am working on DBSCAN algorithm, the original paper is M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise....
0
votes
1answer
1k views

DBSCAN eps understanding problem (and picking correct one in my case)

According to the explanation here, DBSCAN eps value is just the step size, but the resulting distances in the cluster can be much bigger. I think it means, that all the elements of other clusters are ...
1
vote
0answers
238 views

Include target variable for multivariate outlier detection?

Suppose you have data containing a set of features and a target variable you wish to model. You obviously want to remove any outliers from your data. Univariate approaches may not be sufficient as ...
1
vote
1answer
840 views

Scikit Learn DBSCAN with Dice Coefficient

I am trying to cluster a high dimensional data set - Young People Survey Data https://www.kaggle.com/miroslavsabo/young-people-survey This is my first pass and wanted to give clustering the entire ...
1
vote
1answer
958 views

Difference Between Cubic Clustering Criterion, Silhouette Score, and Calinski Harabasz

I am clustering a mixed geological data set containing numeric (pump pressure, bit speed, mud temperature), nominal (presence or absence of a specific stones), and ordinal data (relative concentration ...
1
vote
1answer
761 views

Cluster Validation of Incomplete Clustering Algorithms (esp., Density based - DBSCAN, HDBSCAN)

Context -- Unlike, Partitional clustering algorithms like K-Means, Spectral or Hierarchal Methods, Incomplete clustering techniques like DBSCAN, HDBSCAN and many others have the notion of noise (...