Questions tagged [clustering]

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval etc.

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How to do feature selection for clustering?

I am trying to implement k-means clustering on 60-70 features and I came across a post for feature selection technique on quora by Julian Ramos, but I fail to understand few steps mentioned. I am ...
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Will flattening multivariate time series data before clustering make the results meaningless?

I have a large number of financial time series that I wish to do cluster analysis on. Each time series has the same length and spans multiple years of daily data (returns, volatility, etc.). As part ...
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How do I interpret my result of clustering?

I am working on a clustering problem. I have 11 features. My complete data frame has 70-80% zeros. The data had outliers that I capped at 0.5 and 0.95 percentile. However, I tried k-means (python) on ...
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Modelling data with Machine learning without a target variable

I am new to the data science community and wanted to understand the required steps that need to be taken into account while handling a dataset that does not have a target variable. I can do machine ...
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1answer
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find most dense neighborhood of points in high dimensional space

I'm working on a project where I have many high-dimensional points and I want to find the most dense neighborhood of them. Ideally, out of my ~500 points that are each a 4x300 matrix (300 ms time ...
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What value can I gain by doing exploratory data analysis on features (and thus data) before doing clustering?

This might not be a very good question, but I would still ask if it's beneficial to do EDA before running a clustering algorithm? I understand that EDA helps us generate good and helpful insights ...
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How to deal with with rows with zero in every feature while clustering?

I am working on a clustering problem which has 13000 observations and 15 features. Around 3000 observations in the dataset has zero in every features ( i.e all values zero in 3000 rows). I am trying ...
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Does K - Means clustering on data reduced using PCA and the original data make any difference?

I am working on clustering and I have 90 features with 13500 data points and after removing the correlated variables which had pearson correlation more than 90% my feature space reduced to 70. Also, ...
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Assistance needed on what machine learning approach to use

👋 I'm currently writing my Master's Thesis on Subjective tagging of sounds and I feel that I've been stuck with the same problem for quite a time now and need assistance to progress. I'll, in short, ...
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Is k-means with Mahalanobis a valid option for clustering?

I want more info into if k-means with Mahalanobis distance is a mathematically/methodologically correct option for datasets with different variance clusters. The steps are: Create aggregate datasets (...
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Using TSNE to Visualize Clusters in Python

I'm using TSNE to visualize my clusters but the output seems a bit strange. There are supposed to be 3 clusters but instead, there are 4 lines. Is there something wrong with how I'm visualizing them ...
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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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News de duplication dataset

I am looking for a news dataset with semantically duplicate news articles tagged. Basically all the news articles which talk about the same story should be grouped. The stories can be worded ...
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comparison of t-SNE and PCA and truncate SVD

How to compare the trucate SVD ,PCA, and T-SNE? What we can say about features if t-SNE and PCA and truncate SVD digaram is in this figure?
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What are practical differences between kernel k-means and spectral clustering?

I've been lately wondering about kernel k-means and spectral clustering algorithms and their differences. I know that spectral clustering is a more broad term and different settings can affect the ...
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Identify members who are likely to switch where they receive drug administration

I have access to medical claim data from a large health insurance company. As some of you may know there is a large delta between the price of drug X depending on where it is administered. My ...
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Clustering algorithm which does not require to tell the number of clusters

I have a dataframe with 2 columns of numerical values. I want to apply a clustering algorithm to put all the entries into the same group, which have a relatively small distance to the other entries. ...
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How to save and load model from unsupervised learning?

[Beginner] Sorry if this is dumb question. I am following the model from this article and below. ...
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How to retrain a K-Modes model based on daily data?

I have read that retraining a model depends highly on what you are trying to achieve. I am conscious that maybe I need to retrain my model daily and after a certain time I have to train the model ...
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Best clustering algorithm to identify clusters and determine the closet cluster each individual response is near?

I have a survey where each question is related to a different 'shopper' type (there are 5 types so 5 questions). Each question is either binary (True/False) or scale based. IE: ...
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Coding a Content Addressable Memory on a GPU

I´m trying to code a CAM or more simply a dictionary storing the pointer of the data accessible by a key. I try to do it with a GPU but all attempts have been inefficient compared on using System....
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Tableau: Clustering based on value-range for map coloring

Is there a possibility to cluster coloring for certain statistical ranges? This is what I have been able to achieve so far.
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Clustering Weekday Weekend Data and Multicollinearity

Hi I have data of weekday and weekend step counts in which I extracted metrics from them such as the wd steps, we steps, standard deviation of wd steps, standard deviation of we steps and so on... <...
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Is it possible to run clustering methods by only knowing the distance between pair of points?

By knowing each data point's coordinate, it is easy to apply them with clustering methods as k-means etc. By if the case is we only know the distances between each pair of data points without knowing ...
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What's the good index to choose number of clusters so that obtained clusters are homogeneous?

I perform a clustering on one-dimensional dataset and I need a way to automatically decide what's the optimal number of clusters from $k \in \{2, 3, 4, 5, 6\}$. The number of observations to cluster ...
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Cluster elements that appear in the same lists

Suppose I have a multitude of sets with (unordered) combinations of elements and I want to determine which elements tend to appear together. For example Given the following sets: ...
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Why spectral clustering results in disjointed cluster?

I'm working on a project where I have to dynamically cluster the position of objects with respect to one coordinate. So I'm essentially dealing with subsequent frames and each frame represents a one-...
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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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Are cluster feature and micro-cluster good summury statics for outlier detection in high dimensional data streams?

I'm dealing with outlier detection in data streams. I'm looking for a way to summarize my data and obtain important statistics such as means and variance, etc. I want to know if the cluster features ...
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Is there any good practice to cluster 3D data array?

So I'm not sure what word fits best to describe this data, probably "dimension" would be wrong since it may be used for flat samples with 3 features; but by 3D data I mean some structure in a form of ...
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KMeans clustering for Image Data

I am trying to cluster the sample of Imagenet Dataset using K-Means clustering. In this approach, I have used the below 2 approaches to get the optimal number of clusters. Elbow method From the ...
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clustering changing Label

I have dataset with clustering label (this label is the group of each point) and I want to create such recommendation system or any other model to help the point for changing his group (for example ...
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which algorithm used by sklearn.model_selection.StratifiedShuffleSplit for clustering into n_splits

which algorithm is used by sklearn class StratifiedShuffleSplit for clustering/stratification into n_split. dendrogram looks evident but can anyone suggest any reference. class sklearn....
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Clustering with 0 or Null values

I want to do some clustering for a dataset where I am looking at 10,000 peoples usage of certain electronic devices. I have 11 columns; the first column is simply a URN representing each person in the ...
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Can a machine learning model be trained on Call Detail Record(CDR) Data to predict user's daily locations?

I have a CDR data for two months and my goal is to extract daily or frequent locations(cell towers) of the user along with the departure and arrival time on those locations. The spatial resolution of ...
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1answer
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PCA before Affinity Propagation (AP)

I have a large-ish dataset (100k samples, ~100 features), that I am trying to cluster, to an unknown number of clusters. I thought of using PCA first, to reduce dimensionality, since I understand that ...
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Clustering list of list of integers

I have ~100 sets of samples with integer IDs. For example, 3 of them could be: a = [0, 1, 3, 4, 6...] b = [1, 5, 9, 102...] c = [1, 7, 10, 42...] I am looking to ...
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Clustering initialization

I'm running into a problem while working on clustering. I work on data with white Gaussian noise. All of the methods I have come across use some sort of random initialization to set up the mean and ...
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Hierarchical Clustering on transaction data

Problem Statement: Let's say I have buyer transactional data for every product, features are categorical and numeric. I want to cluster purchases that have similar attributes in terms of who's ...
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Finding differences in smooth embedding spaces?

I'm trying to find statistically significant differences between embeddings (by an autoencoder) of different datasets. At first I tried to embed them separately, and cluster them. However, the ...
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Evaluation of multi-label clustering

I am trying to find some evaluation methods for multi-label clustering. I refer to this paper: Kai Tian, Meghan Revelle, and Denys Poshyvanyk: Using Latent Dirichlet Allocation for Automatic ...
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Transform skewed ratio data (value range from 0 to 1) to reduce the skew

I want data clusters. Because my cluster algorithm doesn't work with skewed data I want to change that in advance. I have ratio data, i mean probabilities (values between 0 and 1). But these data are ...
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1answer
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Topic models for non-textual data?

I am looking to employ an unsupervised clustering on a dataset where each observation has a mix of textual and non-textual features. For each observation, I combine the features into a single vector ...
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248 views

How to evaluate the K-Modes Clusters?

K-modes algorithm is available here I want to do clustering of my binary dataset. I need to specify the number of clusters that I need as an output: ...
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KMeans clusterization on documents

Whether correct or not, I'm not able to judge being myself in the early days of the Data Science. However, I have applied a Kmeans on a corpus where some random documents (very short sentences) have ...
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1answer
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Remove noise by clustering on which step of pre-processing is better?

I am working on a classification task. The dataset is a UCI data set about machine learning with 200 observations and 2 classes. Part of my model includes the following preprocessing steps: remove ...
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How can I improve the results of my clustering

I am working on a project with the idea to cluster the sound waves of key strokes on a computer. So far what I have done was recorded about 50 keystrokes per key (only have done 1 - 10 so far), found ...
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What is an appropriate statistical method for determining correlations between variables with likert scale data?

I am conducting an exploratory analysis with a dataset of about 200 records (all likert scales (numeric)). I aim to determine the correlations between the different variables that the responses ...
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1answer
54 views

Feature transformation possible at selected features or only at all?

I want to cluster. I have different features for that. Some features have a very small value range (from 0 to 0.8) and some have a very large value range (from 0 to 5 million). I want to use the ...
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Evaluate clustering by using decision tree unsupervised learning

I am trying to evaluate some clustering results that a company did for some data but they used an evaluation method for clustering that i have never seen before. So i would like to ask your opinion ...