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.

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ROC and AUC for clustering algorithms [closed]

I am working on some clustering algorithms like DBSCAN and local outlier factor. Now i want to know how can I make ROC and AUC curves from clustering results. Do anyone know how can i make RO and AUC ...
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Clustering DBSCAN's parameter Epsilon: How is eps related to scale of data being clustered?

How is scale of eps related to data to be clustered in DBSCAN? e.g. in image of 1024x1024, we have points as: ...
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Can DBSCAN overcome the drawbacks of K-means?

We have a great post to discuss the drawbacks of K-means. Can DBSCAN overcome these drawbacks? and what are the drawbacks of DBSCAN?
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What are the assumptions of DBSCAN?

Does DBSCAN (Density-based spatial clustering of applications with noise) assume that the data follows a Gaussian distribution? and does the data need to be standardized (mean $0$ and standard ...
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1answer
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Do $k$-means, dbscan, and hierarchical clustering all rely on (pseudo)metrics?

I seems to me that the clustering methods $k$-means, dbscan, and hierarchical clustering all work on distance measures $d$ that are (pseudo)metrics, i.e., fulfill the following requirements: $$ d(x,x)=...
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Cluster large dataset in R

I have a very large dataset (200M observations) with spatial points around the world with a ground resolution of 30m. I would like to assign clusters based on a minimum of 33 points per cluster and a ...
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1answer
28 views

Definition of boundary points in DBSCAN

If I understand correctly, DBSCAN is the following method of decomposing a set, which has parameters $\epsilon$ and $k$. Given a set $E$ of points in $\mathbb R^n$, consider the graph $G$ whose ...
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Clustering Location along with time of the day

I am new to ML and I don't have wide knowledge about ML. My dataset consists, animals visited locations (latitude and longitude) along with the timestamp. I clustered locations by using the DBSCAN and ...
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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 ...
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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 ...
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29 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 ...
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32 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 ...
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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 ...
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1answer
21 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 ...
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40 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 ...
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1answer
33 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 ...
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38 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
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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 ...
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38 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 ...
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39 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 ...
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1answer
251 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 ...
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734 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 ...
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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 ...
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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 ...
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61 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 ...
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1answer
38 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 ...
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215 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 ...
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1answer
74 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 - ...
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430 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,...
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1answer
319 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 ...
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3k 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 ...
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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 ...
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46 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 ...
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1answer
302 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 ...
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101 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 ...
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1answer
1k 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)?
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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 ...
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2answers
861 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 "...
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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 ...
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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 ...
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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 ...
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29 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 ...
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1answer
187 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, ...
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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 ...
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1answer
940 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 ...
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1answer
454 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 ...
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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 ...
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643 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 ...
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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 ...
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1k 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 ...