A message from our CEO about the future of Stack Overflow and Stack Exchange. Read now.
Data Science

Questions tagged [clustering]

Ask Question

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.

850 questions
Newest
Active
Bountied
Unanswered
Filter by
Sorted by
Tagged with
1
vote
2answers
148 views

Intuition behind the fact that SVM uses only measure of similarity between examples for classification

I have read about SVM and although I did not understand the math behind it completly, I know that it produces decision plane with maximum margin between examples of different classes and role of ...
3
votes
2answers
49 views

Clustering of news combining headline and main article

I want to classify German police news articles and do an automated classification/clustering with regards to the kind of crime committed. Thus far I am not getting great results. Often times the ...
1
vote
1answer
20 views

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, ...
0
votes
3answers
72 views

Applying and Visualizing k means clustering on a data set that has 9 features

I had a data set of images that I have extracted 9 numerical features that I want to apply k means clustering or hierarchical clustering to. I'm just not sure how to go about it. The tutorials I have ...
2
votes
2answers
31 views

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 (...
1
vote
1answer
35 views

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 ...
1
vote
1answer
27 views

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 ...
2
votes
4answers
119 views

Which learning algorithms to use in what order - dimensionality reduction, bayesian network structure, regression?

The data is a huge set of observations of dozens of variables, all (potentially, somehow) related to a dichotomous outcome variable, and all (potentially) correlated to each other, or to unknown / ...
1
vote
1answer
46 views

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 ...
2
votes
2answers
32 views

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 ...
1
vote
2answers
29 views

Identifying common keyphrase frequency in large dataset

I have a dataset of profiles which contain freeform text describing the work history of a number of individuals. I would like to attempt to identify frequently used words or groups of words across ...
4
votes
1answer
326 views

Clustering for high dimensional data

I am have a data set with 52 variables. Most of them have zeros, it resembles a sparse matrix. How can I cluster this kind of data and are there any special types of clustering? I am attaching pca ...
5
votes
6answers
3k views

Do Clustering algorithms need feature scaling in the pre-processing stage?

Is feature scaling useful for clustering algorithms? What type of features, I mean numeric, categorical etc., are most efficient for clustering?
0
votes
1answer
141 views

Assigning a new document to a cluster based on keywords extracted and tf-idf

I have about 40 clusters of documents defined by a combination of k-means clustering algorithm and hand curation. For example, some of the clusters given by k-means are too noisy so they have been ...
0
votes
1answer
4k views

Cluster tendency using Hopkins statistic implementation in Python

The Hopkins statistic, is a statistic which gives a value which indicates the cluster tendency, in other words: how well the data can be clustered. If the value is between {0.01, ...,0.3}, the data ...
0
votes
2answers
63 views

Unsupervised Clustering of 6-d vectors

I have 17k vectors, each having 6 points, I want to cluster the vectors based on nature of points, eg. linearly increasing into one cluster, convex in another cluster, concave in another, decreasing ...
3
votes
2answers
67 views

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 ...
1
vote
2answers
57 views

Clustering with sets as values

I have gathered a large amount of qualitative data and am now looking to cluster it so as to make sense of it. For this, I am using Biolab's Orange. In my data, specific values may co-occur in a ...
0
votes
1answer
31 views

Merging three different customer segmentation systems into one

I have been given a task where I have three existing customer segmentation systems (rule based e.g. if customer spends X in Y amount of time AND whatever then put in top spender segment is one segment,...
1
vote
2answers
160 views

RGB Image Segmentation using Clustering

I want to apply some segmentation on a dataset for preprocessing purposes. I have tried the "otsu thresholding" approach in order to segment the image. It's a good method, however, I think a ...
1
vote
0answers
8 views

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....
6
votes
3answers
96 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-...
1
vote
0answers
12 views

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 ...
0
votes
1answer
11 views

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 ...
0
votes
1answer
22 views

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?
3
votes
4answers
260 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. ...
4
votes
0answers
27 views

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 ...
3
votes
1answer
14 views

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 ...
0
votes
1answer
64 views

When to stop collecting data?

I have developed an iterative process via which I can collect data in batches. The data are points in a predefined 3D space. I am trying to explore and locate clusters in that 3D space based on my ...
4
votes
2answers
109 views

clustering plus linear model versus non linear (tree) model

a team has to create models that predict the cost of deploying a machine over time. This is a regression problem. The team is further divided into two groups, A and B. Group A puts lots of ...
1
vote
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. ...
0
votes
1answer
17 views

Testing if a sample fits into an existing cluster

I have a sample of data I'd like to create a model from, which would create N clusters. After the fitting to clusters, I'd like to test various samples against the existing clusters, seeing if the ...
2
votes
1answer
21 views

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 ...
30
votes
1answer
55k views

What is the best Keras model for multi-class classification?

I am working on research, where need to classify one of three event WINNER=(win, draw, lose) ...
1
vote
1answer
189 views

score or cost function for AgglomerativeClustering

I am learning AgglomerativeClustering using sklearn. It is fairly easy to use for example: ...
1
vote
2answers
22 views

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: ...
1
vote
1answer
179 views

How can I adjust the legend when visualizing clusters in two dimensions?

How can I change the legend as we can see now the legend has some cluster numbers missing. How can I adjust the legend so that it can show all the cluster numbers (such as Cluster 1, Cluster 2 etc, no ...
1
vote
1answer
130 views

Conditional clustering

I have a dataset consisting of addresses (points) that have several attributes; one that distinguishes the "sort" of address and one anntribute that contains a numerical value. I want to cluster ...
0
votes
1answer
34 views

confusing regarding to kmeans clulstering for data correlation

I am trying to think through my process before doing any real coding. However, got really confused easily. Say I have 100 instruments and I know their price movements every day for a year. So I can ...
1
vote
0answers
11 views

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....
0
votes
1answer
894 views

How to measure F1 score and NMI for clustering task?

I see the authors of this paper are measuring F1 and NMI scores to measure the clustering quality. However, I don't understand the algorithm of how they actually measure it. See the Evaluation Section....
2
votes
2answers
64 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 ...
0
votes
2answers
69 views

Sourcing (discounted) products customers want

Goal: Generate a list of 100 products per vertical (e.g. fashion, electronics) that the teams should source, discount, and list on the website over a specific period. You may assume all customers are ...
1
vote
1answer
78 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.
0
votes
2answers
67 views

I have 32k black and white images. Want to do clustering on them

As the title says I'm trying to do clustering on a set of black and white images. These images are all 200x200 with black dots on a white canvas Example pics here (These are not actual photos from the ...
0
votes
2answers
465 views

Anomaly detection on multidimensional time series

I have relatively little knowledge of unsupervised machine learning. I'm working on a project that aims to find anomalies in a set of n data, measured every ...
2
votes
1answer
27 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: ...
2
votes
1answer
16 views

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... <...
4
votes
2answers
109 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 ...

1 2 3 4 5 … 17