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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Ant colony optimization for clustering

What do you mean by applying ant colony optimization (ACO) to clustering? What is the output one would get after it? Could you explain it using a two dimesional data set which is clustered into 3 ...
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ARI (Adjusted Rand Index) result meaning

I am using K-Means for a binary classification problem in labelled data. I think that K-Means used opposite labels to mine for the output variable. I calculated the ARI to better understand if the ...
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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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What would cause a Hierarchical Cluster to look like skewed (if this is the right term to use!)?

I am surprised about this hierarchical cluster! For me, it looks somehow abnormal. Or, maybe normal but I am not able to identify why it looks like this. Any idea why data would be clustered in such a ...
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Clustering based on features of varied importance

Suppose I have a dataset that includes the following features {HairColor, EyeColor, EducationLevel, Income}. I would like to perform clustering to separate the dataset into smaller datasets that you ...
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Using variance Threshold removes all my features (clustering)

I have 100+ features for clustering. I am unfamiliar with unsupervised tehcniques for clustering. I have heard of variance threshold. However the features i have used have low variance.. e.g. some ...
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How can i decide on which features to use for clustering?

I am clustering on a dataset where each row is a customer and each column is a feature. I have 200 features, this seems like alot for clustering. I plan to experiment with a variety of clustering ...
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I know which forums specific user read - how to cluster the data?

I have a dataset as follows. For each user, I have a separate row with the forum he reads. There is up to 100 different forums. I would like to cluster this data, so each user will be assigned to one ...
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Perform clustering from a similarity matrix

I have a list of songs for each of which I have extracted a feature vector. I calculated a similarity score between each vector and stored this in a similarity matrix. I would like to cluster the ...
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Classification based on color clustering

I need to classify some domain specific images by analysing their color distribution. I have annotated data; this last classification step is supervised. After some preprocessing and masking and other ...
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Cluster images labels in some given categories using word embeddings

Given: set of images Labels in string format each one. Also I've given a set of Categories, also in string. ($Images \neq Categories $) Goal: I need to map given labels to given categories to "...
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How to cluster words automatically?

I have a problem where I have a list of n words with truly k different ones (k is unknown) because some may be malformed or contracted. I would like to automatically cluster them. I thought about ...
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Algorithm for clustering worm-like points in a cloud

I have a cloud of points were I can clearly identify worm-like patterns in the cloud, along the Z-axis but embedded in the noise. My goal is to cluster the points that belong to the same "worm'. ...
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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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Can I cluster an aggregated data-set (grouped by) and apply dimensionality reduction?

I have data of sales, however it is in the millions, about 500M rows. I aggregate this data by factors such as location, shoptype, country_of_shop, cardtype, and then the aggregated statistic is: ...
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Density estimation of hashed strings grouped by events

Beforehand, sorry for might not use the correct terms, but this is my first adventure in the Data Science world. Said that this is what I'm searching for: I have data (strings) that will be hashed. ...
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Incorrect visualisation using Plotly

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Explaining the similarities between instances in a cluster with KMeans

If I create clusters using the KMeans clustering algorithm in Python, is there any way I can find which attributes were used to group those instances in clusters? Example: I have a dataset of cars ...
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How can i interpret cluster attributes from clusters in hierarchal clustering?

I have data with numerical columns for products bought e.g. price, etc, many are numerical but it is also crucial i incude the industry classification of product e.g/ fashion, gardenware etc. If i use ...
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Active learning with mixture model cluster assignments - am I injecting bias here?

Suppose I have a dataset of people's phone numbers and heights, and I'm interested in learning the parameters $p_{girl}$, $p_{boy}=1-p_{girl}$, $\mu_{boy}$, $\mu_{girl}$, and overall $\sigma$ ...
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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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How to figure out how variables affect undulating sequences in a plot?

Let's say I have a file named accepted_D.dat that looks like this. This file gives 7247 and variables that describe them: header: "model ID", "Z"...
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Create clusters based on specific keywords

I am working on raw text data. I am using clustering to put together common words in the documents. My requirement is to create clusters based on a specific list of words i.e I want to get a group of ...
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R: How to find the rows in the input files that correlate to the 2 “populations” in the plot

I am completely new to clustering analysis. Let's say I have a file of the format: ...
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Pattern detection in sequence of users behaviour for clustering

I need to detect variable-size patterns in a set of fixed-size strings, where each string represents a sequence of activity of a group of users. As an example, i have a 10 character string or user 1 ...
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When should we choose agglomerative clustering over K-means clustering?

I was working on a clustering based model and I read about hierarchical clustering and K-Means clustering. Under what conditions should I choose agglomerative over K-means clustering?
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Find 10 most similar data points from 100?

I am trying to solve a problem where I need to group battery cells that are most similar to each other to form a reliable battery pack. It seems like a clustering problem but I only need to find the ...
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What is the intuition of using clustering for performing feature engineering in machine learning tasks?

I am trying to implement the research paper Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance. The paper has a section called meta feature engineering where they have ...
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Which algorithm would be suitable for clustering a billion datapoints?

I am running a K-means algorithm (using the sklearn implementation) on an aggregated dataset of ~350k datapoints on a 6 dimension hyper-plane (using 6 features). I ...
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Feature Importance and cross Validation in Unsupervised Model

I am working on creating unsupervised machine learning model, and I have some questions. How to get features importance from the dataset? Can I apply cross validation or train test split in ...
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How to find the optimal number of clusters for K-modes? : Python

I have a categorical dataset (survey data on a likert-scale: never to extremely often). So, I need to cluster them. Therefore, I am planning to use K-modes. But, I am stuck at selecting the optimal ...
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How meaningful are the results when you difference the time series dataset before clustering?

On a certain task where I need to perform K-Means Time Series Clustering with DTW algorithm, I would like to know how credible the results are when performing clustering on the original vs a dataset ...
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what indicators can be used to classify a group of stocks?

I am working with a dataset contains the daily return time series of 50 different stocks, I want to divide these stocks into several different groups. My idea was to make a new dataset contains some ...
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Is feature importance from classification a good way to select features for clustering?

I have a large data set with many features (70). By doing preprocessing (removing features with too many missing values and those that are not correlated with the binary target variable) I have ...
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Accuracy of a Cluster in Tableau

I have certain data , in that I have labels and formed a groups , Also with the same data without using any labels I used clustering option that is available on tableau . So, Now I want to check the ...
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Finding the dual to an optimization problem on an unsupervised dataset [closed]

We consider the unsupervised dataset $x_1,..x_N \in R^d$ and the optimization problem: $$min_w \,\frac{1}{2}{\left\lVert w \right\rVert}^2,$$ subject to constraints:$$\forall_{i=1}^N: \phi(x_i)^Tw\...
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Clustering sets based on common elements

I am looking for a clustering algorithm for the purposes of combining routing information. Suppose we have the following sets: A={1,2,3,4,5} B={2,3,4,6} C={3,4,7,8} D={8,9,10,11} If we want 3 groups, ...
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Classification and variable selection with a single known class

I am looking for help and suggestions on how to approach a classification and feature selection problem making use of a single define class and several unknown ones. Using an example, for those than ...
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Clustering pre-defined groups of data points under dimensionality reduction

I have a dateset of around a million observations, and each observation (300 features) belongs to one of around 300 groups. The set of observations of one group does not directly correspond to the ...
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what is the difference between strong and weak clustering?

What is the difference between strong and weak clustering? and what algorithm is considered as strong and weak clustering? Is fuzzy c-means and bisecting k-means considered as strong clustering? I ...
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Predictive Clustering in R with Mixed Data

Does anyone know of a predictive clustering procedure in R for mixed data types? For example, suppose that I had conducted an unsupervised k-prototypes procedure in order to estimate clusters for ...
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Identifying hierarchical structures from user submitted tags

I am working with a dataset of shops, where users have submitted some number (up to 5) of "tags" for each shop, which is supposed to describe an aspect of the shop. From the tags, I want to ...
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Hierarchical clustering with the consensus matrix as similarity matrix

I'm following this article on consensus clustering in Python programming. On page 7 the authors state that "The consensus matrix lends itself naturally to be used as a visualization tool to help ...
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Consensus clustering: how to choose the final cluster based on the consensus matrix?

I've been reading about consensus clustering and the consensus matrix in this article. I understand how the consensus matrix is made after re-sampling and clustering parts of your data H times. I ...
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Can distortion be derived from inertia rather than recalculating it from scratch in case of kmeans?

I got this definitional difference between distortion and inertia from here: Two values are of importance here — distortion and inertia. Distortion is the average of the euclidean squared distance ...
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Classification of multidimensional data to multidimensional clusters with a varying subcluster structure

I have a large dataset with mixed (numerical, categorical, textual) data that I need to classify. The clusters are well-defined, but multidimensional (i.e. vector-valued) and have a varying structure ...
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Three different errors using external information. Which one makes sense? (Or how to interpret each?)

My goal is to compare clustering methods considering different method and different number of clusters using an external information. Could anyone please give some opinion/ recommend book/paper about ...
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How to group clusters with semantic similarity?

I have a list of job titles. I found the semantic similarity between them by using word2vec in spacy. Now I want job titles ...
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How to cluster texts by most relevant words

I have a huge amount of documents and every document has its own portrait, where a portrait has this structure (document_id, word, weight). TFIDF, basically. I want to cluster these documents into ...
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What is the best way to cluster this kind of data?

I have data that looks like this: The chart on the left is the trend and the smaller chart on the right is the box plot showing the distribution of means. Each color is the output of a particular ...

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