Questions tagged [dbscan]

DBSCAN means density-based spatial clustering of applications with noise and is a popular density-based cluster analysis algorithm.

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How to add 'other' as one group to clustering algorithm inference pipeline

I have few clustering algorithms tuned having 5 cluster. I want 6th cluster if new data does not belong initial 5 cluster fall in 6th cluster. 6th cluster [ say other category] consist of all data ...
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Difference in threshold values for DBSCAN and Threshold clustering

I am trying to cluster similar faces using Facenet embedding approach. I am extracting a 256 feature vector using Facenet model on a standardized labelled Celeb-faces dataset, and trying cluster using ...
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How to group every data point with HDBSCAN to some group to have no noise?

TASK I am clustering products with about 70 dimensions ex.: price, rating 5/5, product tag(cleaning, toy, food, fruits) I use HDBSCAN to do it GOAL The goal is when users come on our site and I can ...
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Feature scaling for clustering

I want to cluster groups, using K-Means, DBSCAN, etc. algorithms, based on lat-lng coordinates along with other features such as dummy variables, continues variables (in different units). What would ...
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Comparison between DBSCAN and single linkage hierarchical clustering

I am studying clustering algorithms and I want to find a good example where single link hierarchical clustering algorithm returns better cluster results than DBSCAN (having provided valid parameters). ...
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76 views

Estimate eps value in DBSCAN using KNN algorithm

I would like to estimate the best eps value for the DBSCAN algorithm on this dataset by following this set of rules: Set a minPts: 10 Compute the reachability distance of the 10-th nearest neighbour ...
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1answer
86 views

Understanding and find the best eps value for DBSCAN

I'm trying to run the DBSCAN algorithm on this .csv. In the first part of my program I load it and plot the data inside it to check its distribution. This is the first part of the code: ...
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317 views

Clustering Tweet Data using DBSCAN Algorithm

I am doing a tweet clustering using DBSCAN algorithm. I use all the preprocessing steps and convert sentences to vector format before applying the algorithm. ...
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45 views

DBSCAN on textual and numerical columns

I have a dataset which has two columns: title price sentence1 12 sentence2 13 I have used doc2vec to convert the ...
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Building an unsupervised learning model to detect suspicious transactions using DBSCAN [closed]

I am working on building a unsupervised learning model to detect suspicious transactions using DBSCAN. Do I train the model on all data columns (columns like account number, transaction date, ...
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27 views

How to study the effect of eps in sklearn.cluster.DBSCAN?

I posted this question on stackoverflow.com and have not received any answer. In case I get an answer from one of them, I will inform on the other. I have a dataset and is requested by my professor ...
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Looking for spatial clusters and anomalies. Is DBSCAN the right tool?

I have a regular 2D grid of data points (X, Y) with each point having a value. I'd like to identify clusters and then anomalies that don't belong to those clusters. I'm trying to understand the best ...
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164 views

Shall I use ordinal encoding or One-Hot-Encoding when using DBSCAN for content clustering on websites?

I want to cluster the preparation steps on cooking recipes websites in one cluster so I can distinguish them from the rest of the website. To achieve this I extracted for each text node of the website ...
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DBSCAN - Best way to find the Eps and MinPts for geospatial data (coordinates)

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 ...
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DBSCAN Clustering

I used K-means to get the number of clusters for my data(Elbow Method). Then I was trying to see if for some specific hyperparameters can we get the same number of clusters for DBSCAN. I tried Brute-...
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How may I may fit the cluster number while it is going to the outside of the X-axis and when visualizing clusters in two dimensions?

I am writing code for DBSCAN clustering. I find the eps value which is 0.12 with the help of ...
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2answers
792 views

Nice real data sets for testing DBSCAN?

I'm looking for real datasets on which I could test my DBSCAN algorithm implementation, that is, a dataset of points in (ideally 2 dimmensional) space, or a set of nodes and info about the distances ...
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Types of artificial anomalies

I am working on some algorithms for anomaly detection The dataset is clean our anomalies so I want to add some artificial anomalies. I have added some anomalies. I get the maximum value of the ...
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Classifier for DBSCAN [closed]

I have written a code that uses DBSCAN and tries to find the most appropriate eps for my dataset, trying to include most of the data inside a cluster. The problem is that DBSCAN is not a classifier ...
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How to use Cosine Distance matrix for Clustering algorithms like mean-shift, DBSCAN, and optics?

I am trying to compare different clustering algorithms for my text data. I first calculated the tf-idf matrix and used it for the cosine distance matrix (cosine similarity). Then I used this distance ...
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Estimating minPts in DBSCAN for document layout clustering

I am trying to choose parameters for DBSCAN clustering algorithm, in particular minPts. The Wikipedia article suggests a rule of thumb to derive minPts from the number of dimensions D in the data set....
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Plots of data using DBSCAN algorithm not making sense

I am using clustering for my data. Since the DBSCAN algorithm will also tell me an estimate of clusters that I can use, I have used DBSCAN. I have tried for the ...
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99 views

Clustering Small Text Descriptions

Im presented with a unique text classification problem. Im given a list of descriptions each containing 3-8 words. I know that there are some descriptions that are nearly the same, but the majority ...
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63 views

what arguments should I pass to dbscan or optic in order to divide the data in a specific way

I have thousands of very similar datasets that needs to be divided in diagonal way to two groups. for example: and I tried to play with the argument of dbscan and optic as epsilon and minPoints and ...
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79 views

Is it safe to use labels created from unsupervised model to train a supervised model using the same data?

I have a dataset where I have to detect anomalies. Now, I use a subset of the data(let's call that subset A) and apply the DBSCAN algorithm to detect anomalies on set A.Once the anomalies are detected,...
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343 views

The actual results and results from pickle files are not matching in pandas for DBSCAN clustering

I've built a DBSCAN clustering model. The output result and the result after using the pickle files are not matching. Based on HD and MC column, I am clustering WT column. ...
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91 views

T-DBSCAN - Implementing STOP logic

I am attempting to implement the T-DBSCAN algorithm described in T-DBSCAN: A Spatiotemporal Density Clustering for GPS Trajectory Segmentation. I have been able to implement most of the logic between ...
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Are DBSCAN and dbscan from the sklearn.cluster package different?

I'm new to DBSCAN. I was looking at a few examples online and came across a few instances where the following lines were used while importing the dbscan module: <...
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787 views

How to properly use approximate_predict() with HDBSCAN clusterer for text clustering (NLP)?

I have approached text clustering using HDBSCAN based on this article which describes how to do this in R. I've re-written this in Python using this library. The approach is to first calculate TF-IDF ...
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DBSCAN: How does a quantile of kNN relate to the share core points?

I read this answer by Anony-Mousse to an other question related to density based clustering and how to potentially come up with an eps. It states, that if you want 90% of you points to be core points, ...
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640 views

DBSCAN clustering on document [updated]?

I am new in topic modeling and text clustering domain and I am trying to learn more. I would like to use the DBSCAN to cluster the text data. There are many posts and sources on how to implement the ...
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169 views

Clustering based on geolocation pair

I am trying to process a large set of location data where a list of start and end coordinate is given. For example, ...
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56 views

How to find anomalies/outliers in Panel Data?

I have panel data based on 900000 different entities with 384 time steps and the data is not normally distributed. I am looking for outliers/anomalies, this is unsupervised as I have no examples of ...
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1answer
93 views

Preparing dataframe to carry k-means clustering [closed]

Im trying to apply 3 different algorithms of clustering on my dataset. to check which one fits the best. I'm confused how should I convert my dataframe -k-means -DBSCAN -hierarchical clustering ...
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8k views

KMeans vs. DBSCAN

I am trying to understand some basic clustering techniques. What is the main difference between KMeans and DBSCAN? Can we use both techniques for the same problem?
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How do we interpret the outputs of DBSCAN clustering?

I am starting to learn DBSCAN for clustering but the interpretation part of it seems to be tricky to understand. ...
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34 views

Measure of variety within list/cluster

I have a dataset of about 53000 points. It has been clustered twice, based on two sets of unrelated attributes. For the first clustering (clustering 1) I used DBScan, and it ended up with about 700 ...
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1answer
382 views

How do I right feature selection for DBSCAN?

I want to use DBSCAN to recognize any clusters within all text elements from the DOM tree of any webpage. For example all menu items shall be clustered separatey to all main content or footer elements....
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166 views

Clustering 1-d array with constraints?

I have following kind of 1-d array data to cluster with a few constraints: The array has length from 50 to 300, floating, some of them close to 0 and some far away. Goal: divide the array into n ...
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1answer
75 views

Is there an oriented clustering algorithm?

I'm looking for a clustering algorithm that will make cluster depending on a orientation. The DBSCAN algorithm cluster points based on a constant radius : https://upload.wikimedia.org/wikipedia/...
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164 views

Clustering events in a sequence.

I've a sequence of recurring events I want to group together into representing different operation activities of the underlying process. 1) These events might potentially have an order in their ...
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574 views

Is my data good for (DBSCAN) clustering?

I have a particular dataset consisting of 50k elements with 40 features each. I want to try to cluster the data as it is, without any dimensionality reduction. The main algorithm I am considering is ...
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1answer
963 views

DBSCAN - Space complexity of O(n)?

According to Wikipedia, "the distance matrix of size $\frac{(n^2-n)}{2}$ can be materialized to avoid distance recomputations, but this needs $O(n^2)$ memory, whereas a non-matrix based implementation ...
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3answers
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What is slowing down classic DBSCAN algorithm

How to apply CSR Matrix on DBSCAN algorithm in python without using any libraries? Update: Matrix size (8580, 126356) I have given a shot and implemented the algorithm. It runs rather slow. I guess ...
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167 views

Clustering of variants of similar news articles

We have data of several news sites, having quite literally millions of entries. As each news site publishes their own version of the news (also each news site may publish several different version of ...
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1answer
7k views

Python clustering and labels

i'm currently experimenting with scikit and the DBSCAN algorithm. And i'm wondering how to combine the data with the labels to write them into a new file. I'd also like to understand how the labels ...
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1answer
47 views

How to select the second point where derivative is greater than 1 in r? [closed]

I'm looking for the exact value of epsilon to run the DBSCAN clustering algorithm. Here's the KNN distance plot. This chart ...
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2answers
43 views

Testing unsupervised clustering

Assume we train a KMeans model using data X. This will give a set of centroids that can be used to cluster data X* using a Nearest Centroid Classifier. If we use a density-based model such as DBSCAN ...
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660 views

Scaling DBSCAN clustering - minHash?

Applying density based clustering (DBSCAN) on $50k$ data points and about $2k$-$4k$ features, I achieve the desired results. However, scaling this to $10$ million data points requires a creatively ...
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252 views

Clustering documents - how to evaluate results?

I'm using DBSCAN clustering on a set of documents. The documents' content was converted to TF-IDF matrix, and I'd like to find consistent ways to evaluate the clusters when no added information is ...