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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12 views

Clustering of sparse matrix with many co-variates

I have a 2M x 2000 sparse matrix where rows represent an item and columns represent dimensions. I want to understand whether there are meaningful clusters in the data and I started to explore the ...
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Is there a way to recognize which of these scatter plots is “better”?

I doubt better is the correct adjective, apologies for that. What I mean is this: I have a set of files (1200~) each paired with a scatterplot image. I need to find a way to classify which data files ...
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How to cluster categorical and numerical data in the same dataset?

I have a dataset in which it contains both numerical and categorical data. This can be done using supervised learning algorithms, but I am eager to see how this data can be clustered using some ...
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Is there a method to cluster multivariate time series with differnet number of variables?

Say, there is a dataset of timesries. The majority of timeseries has 4 variables. Occasionally, a timeseries can has a meaningful extra column. So, how to deal with a not constant dimensionality of a ...
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Clustering algorithms in pre-processing for classification problem

Through experience it was found that using k-means does not give accurate results to use them in pre-processing of classification, so if I use another clustering algorithm, can results be more ...
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Visualizing the difference of a set of strings

I have a distance metric on a collection of strings on the order of tens of thousands. What would be an intuitive way to summarize how 'different' these strings are or when they overlap? My goal is ...
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Clustering and producing final results to find next best customer to target(Ranked)

I have a problem where I need to cluster customer data that has all possible attributes to identify the next potential customer who can succeed the last customer in terms of buying a certain product. ...
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2answers
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Clustering data set with multiple dimensions

I have a data set which is similar to the following: It is recipe data along with the composition of the recipe (in %) I have 91 recipes and 40 ingredients in total. I want to be able to cluster ...
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2answers
44 views

K-Means Clustering for data points with multiple attributes

I'm very new to K-Means clustering. Every example that I have seen has a two-dimensional data set. I am working to classify recipes of varying ingredient composition into families. Each recipe is ...
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25 views

How to apply K-Medoids in PySpark?

the pyspark ml library does not provide any clustering methods for K-Medoids. So my question is, how can one apply K-Medoids in a pyspark context?
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Attractive clustering experiment for missing value datasets

I have an upcoming publication, where I present an algorithm for clustering tabular datasets with missing values. I want to do quantitative evaluations of my algorithm and qualitative evaluation. In ...
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kmean clustering

I am having a problem in Matlab. I would like to use kmeans clustering and then get the value and index of the centroid. For example, if there is an $5*5$ array, we do kmeans clustering where k=2 and ...
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27 views

How to analyse player and enemy position for data analysis

I am trying to analyse a Serious Game for students learning. In one of the game levels, There are multiple positions like Player Position (x, y, z), enemy position (x, y, z), Player shot at position (...
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1answer
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Are there any examples other than anomaly detection where unsupervised deep learning could be useful?

I am new to deep learning and its concepts. After reading a while I understood that unsupervised deep learning techniques usually try to reconstruct the input data(probably with less number of ...
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1answer
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Recommender system that matches similar customers with similar highly rated products?

I have a dataset of 1,000 customers that bought 20 distinct phones and rated them 1-5. I have several demographic attributes for these customers (gender, age). My website offers 100 distinct devices, ...
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How to find top N neighbors of a datapoint in a cluster sorted in increasing order of distance from that point?

I am doing a clustering exercise and I am doing it using K-Means. After doing the clustering part, I have a dataframe that looks something like this : ...
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How to do feature selection for clustering and implement it in python?

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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2answers
86 views

Modelling data with Machine learning without a target variable

I want 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 learning on top of a labeled dataset having ...
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2answers
30 views

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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1answer
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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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1answer
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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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1answer
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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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4answers
267 views

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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1answer
39 views

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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1answer
83 views

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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1answer
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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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2answers
111 views

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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35 views

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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100 views

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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1answer
17 views

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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1answer
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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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2answers
68 views

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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