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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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Comparing K-means and DBSCAN clustering method

first I want to compare K-means and DBSCAN with my data - well, I have datasets that have 28 feature each of which was continuous. so I apply PCA to reduce the feature and I select each component ...
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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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51 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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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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41 views

hdbscan on numerical AND categorical data (of high dimensionality)

I performed regression on a dataset of motorbikes, where I try to explain their price based on some numerical features (hp, ccm, age, km) and also their model, which is categorical with high ...
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77 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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18 views

DBSCAN loops one or several times a data point?

I am trying to construct a model data to facilitate the clustering algortihms execution in terms of searching for data point in the dataset. This model is a set of connections between points such that ...
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714 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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88 views

Clustering Noisy Data [closed]

There are various ways to cluster data. Some require the data first to be scaled to have a mean of $0$ and standard deviation of $1$. However, others do not mention if the data should be scaled at all....
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243 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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1answer
471 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 ...
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430 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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972 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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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
102 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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696 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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532 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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233 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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60 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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204 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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1answer
964 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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1answer
488 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 ...
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1answer
810 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 ...
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177 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 ...
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2answers
1k 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....
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697 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 ...
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143 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 ...
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557 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 ...
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1answer
590 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 ...
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1answer
512 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 (...
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3answers
562 views

Image Clustering by date and location

I have Latitude ,longitude and timestamp of image. I want to cluster these image according to detect events. For instance I went to Paris for 4 days then london 3 days and so on. So I want to detect ...
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25 views

If x and y are directly connected, does it also hold if x is density reachable from z and y is directly density reachable from z?

I am trying to understand the concept of DBScan, the premise is as follows: 1. y is directly density reachable from x (where x is a core point 2. y is density reachable from x 3. y is density ...
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DBSCAN exponentially distributed variables

I'm studying machine learning clustering algorithms by using a real example of project that the company I work for had to deliver. The goal was to cluster a group of 4920 cities according to their ...
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1k views

Does any other clustering algorithms take correlation as distance metric (apart from Hierachical)?

I have used correlation metric as distance measure for hierachical clustering and obtained the clusters. I used scikit (python 3.5) for clustering hierachical cluster. Now, I want to use another ...
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5k views

Why are most of my points classified as noise using DBSCAN?

I'm using several clustering algorithms from sklearn to cluster some data, and can't seem to figure out what's happening with DBSCAN. My data is a document-term matrix from TfidfVectorizer, with a few ...
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1answer
1k views

Document clustering with DBSCAN

I'm building a document clustering tool which will be fed with datasets of variable size (from several hundred to several million). Documents are represented as dense vectors in (around) 100-...
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336 views

how DBSCAN can be used with track data/trajectory data

I need to apply DBSCAN clustering on trajectory data eg collected from RFID readers from RFID tags at various points in the path defined. HOw can I do it using R ?
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DBSCAN clustering - How to choose “Distance” parameter [duplicate]

I have a dataset where each data point is characterized by two variables: X(time) and Y(volts). When I plot my data it would be something like this: The plot shows some data points created a ...
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112 views

How to cluster trips, i.e. directed lines on a plain

I need to cluster transports/trips based on their start point and end point in longitude/latitude. I have about 5000 trips. Each has a starting point (lon/lat) and an end point (lon/lat). I computed ...
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how was neighborhood distance was calculated for DBSCAN for temprature anomaly

I was going through this below article and was wondering how to calculate neighborhood distance for dbscan with only temperature is available. https://www.researchgate.net/publication/...
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283 views

How to calculate epsilon for DBSCAN when there is only one dimension

In the examples of DBSCAN I have see the epsilon is calculated using euclidean distance I am trying to find outlier of system metrics. I am trying to implement something similar to http://techblog....
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6k views

What is the interpretation of eps parameter in DBSCAN clustering?

I want to cluster lat-long data such that all clusters formed will have radius<=1000 meters Questions What is the actual meaning of eps parameter? Please given an example. Will setting eps=1000 ...
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2answers
1k views

How to implement density-based clustering?

I’m looking to implement density-based clustering with R or Mathematica on a giant file (600,000 points on a 3 billion x 3 billion plane). Is DBSCAN the right method for data that is this sparse? I am ...
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1answer
1k views

Optics\dbscan produces cluster size smaller than minPts

I'm using optics from dbscan package: ...
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7k views

True positive, false negative, true negative, false positive definitions for multiclass-multilabel classification?

I'm trying to apply some evaluation metrics to several clustering methods. I thought that I knew them basing on the multiclass confusion matrix, considering the rows as the actual classes and the ...
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1answer
2k views

Variables involved in kNNdistplot (dbscan package) in R

I have a time-series of a feature(metric) for 4 different servers each of length 2000. I want to use dbscan algorithm to figure out if all 4 machines fall in the same cluster or not using dbcscan on ...
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2k views

Estimate Epsilon in DBSCAN with k-nearest neighbor algorithm

Following DBSCAN paper (quote below), I'm trying to develop a simple heuristic to determine the parameter Epsilon with K-nearest neighbors (k-NN) algorithm. For a given k we define a function k-...
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4k views

DBSCAN: What is a Core Point?

I have a question about DBSCAN. The points here are classified as core points, border points or noise. A point p is a core point if at least minPts points are within distance ε of it, and those ...
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975 views

How to calculate distance between points in DBSCAN matrix data? [closed]

I'm making a simple C implementation of DBSCAN following his pseudocode. If I well underand how DBSCAN works, I may represent my set of N elements (each with M features) with a NxM matrix. When it ...
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1answer
193 views

Proper dataset format for K-Means and DBSCAN clusterers

I'm trying to classify web traffic using clustering algorithms with my own C program, capturing packets with libpcap. In this article K-Means, DBSCAN and AutoClass ...