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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771 views

Building a large distance matrix

I am trying to build a distance matrix for around 600,000 locations for which I have the latitudes and longitudes. I want to use this distance matrix for agglomerative clustering. Since this is a ...
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634 views

Are there any public datasets about mental health of patients containing physiological and psychological symptoms?

I would like to segment mental illnesses with clustering using machine learning. To do so I need training dataset which contains physiological and psychological symptoms of an subject. The closest ...
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1answer
2k views

Clustering based on distance between points [closed]

I am trying to cluster geographical locations in such a way that all the locations inside each cluster are at max within 25 miles of each other. For this, I am using Agglomerative clustering. I am ...
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2answers
264 views

Variable Importance in unsupervised anomaly detection algorithms

I am working on an anomaly detection problem to detect fraud in insurance claims. I have used the PyOD package and used algorithms like ABOD, CBLOF, Isolation Forest, and AutoEncoder. I couldn't find ...
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2answers
145 views

Is it wrong if I cluster numerical attributes and categorical attributes separately?

I have a dataset of credit customers containing mixed data types (numerical and categorical with several levels). I am trying to perform segmentation so that I can end up with k groups and then build ...
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2answers
748 views

k modes: optimal k

I have categorical data and I'm trying to implement k-modes using the GitHub package available here. I am trying to create clusters in my (large) dataset of say, 5-7 records, each of most similar ...
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1answer
169 views

What does Make Density Based Clusterer in Weka do?

In Weka, there is a clustering algorithm with the name as Make Density Based Clusterer. When going through its properties, it takes a clusterer as base clusterer(I took it as K-means with k=3). It ...
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1answer
201 views

How to calculate a weighted Hierarchical clustering in Orange

I am doing my first cluster analysis with Orange (which I recently discovered and looks promising for this iterative and interactive process). Apparently, there are several methods of creating ...
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2answers
373 views

K-modes implementation in pyspark

I'm looking for an implementation of k-modes in pyspark. I found this and this as implementations. First, I tried implementing k-modes using the first link and faced issues. So I went ahead and tried ...
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1answer
39 views

Define value of a centroid

I want to choose a clustering algorithm for which I can define the value of centroids (and not only the number of them). Which algorithm should I look at? My understanding of k-means is that I can ...
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69 views

Best classification technique for following kind of data set

I have a large table where each record or row represents a single salesperson, and there are 50 columns or dimensions where each column represents one of 50 products potentially sold by any given ...
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1answer
37 views

Cluster Analysis - Comparing Same Individuals Clustered Across Different Datasets with different features

I have an interesting problem, and I think my Google is failing me since I can't find the same problem anywhere. I have a set of individuals. I have 4 different datasets, with (some) to (all) of ...
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3answers
96 views

Are there algorithms for clustering objects with pairwise distances, without computing all pairwise distances?

I'm looking for a clustering algorithm that clusters objects, by using their pairwise distances, without needing to calculate all pairwise distances. Normally pairwise clustering is done like this: (...
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1answer
88 views

Clustering by Distributions of Groups of observations

I have data as shown below. Like the groups shown A,B,C.. there are roughly 5400 groups of data and 15 observations for every group (I sampled 6-7 for each group and pasted them here). ...
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36 views

Clustering and graphing similarities of sentence subjects

I have a bunch of sentences. Each sentence is given a weight of how "close" it is to a particular subject. Ex. "I love reading math books" Subjects for the above sentence = ...
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1answer
52 views

Why in spectral clustering number of eigen vectors is same as number of clusters that we want?

In spectral clustering we take eigenvector corresponding to K smallest eigenvalues. Then we do K means clustering on these eigenvector to get final clusters. What will happen if we take different ...
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1answer
230 views

Use output of K-Mean for Logistics regression

I've created a binary classifier using K Mean, which predicts fraud and legitimate accounts, 0 and 1. This uses two features, let's say, A and B. Now, I want to use other features like C and D, to ...
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1answer
196 views

Hierarchical Clustering and Variable Selection

I am using "Single linkage" hierarchical algorithm to cluster my data points with Gower Distance as my data have both qualitative and quantitative variables. After applying this for the full ...
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3answers
1k views

Clustering algorithm for a distance matrix

I have a similarity matrix between N objects. For each N objects, I have a measure of how similar they are between each others - 0 being identical (the main diagonal) and increasing values as they get ...
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1answer
47 views

Scikit learn kmeans with custom definition of inertia?

I've coded a small clustering algorithm for time signals using kmeans, which works ok (gives acceptable results). However, kmeans uses the sum of squared differences. I would like to be able to input ...
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0answers
39 views

Anomaly detection in structured textual data

Pls refer screenshot for sample data. As can be seen most of the fields in data are textual and highly correlated but each row has unique values and hence won't be right to call it categorical. I ...
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0answers
21 views

recommend new category paths based on factor item matrix and sales of the items

Matrix A be a user item matrix. Upon performing UV decomposition, I have just the V matrix. The matrix A differs every week and I get a new V matrix every week. The matrix U is not kept track of and ...
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2answers
507 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 ...
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1answer
44 views

Which algorithm or tool to use to classify as good or bad?

I have a feature vector with different data types, Considering all the data in that feature vector. I have to classify as Good or Bad. Which algorithm should be used to just get the output Good or ...
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1answer
29 views

Finding the best “depth” of ICD9 codes with pseudo-hierarchical clustering

Here is a common problem in health care modeling. Did I just invent a new algorithm or has someone already thought of this? The goal is to find the most homogeneous partition of patients by medical ...
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1answer
36 views

What is the best to identify the proper hierarchy of this data?

So I worked on a hierarchical clustering algorithm to be able to determine which items are most similar, and what attributes are most important. I have two tables: Table 1: contains a bunch of item ...
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4answers
73 views

After running K-means on 12 features, I get an array containing clusters for each row. What is the next step after this?

So I used the elbow method to identify the optimal number of clusters, i.e. 4 in this case. After running K-means on dataset with 12 features, I get an array with the cluster number each row belongs ...
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2answers
3k 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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2answers
1k views

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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1answer
524 views

Kmean clustering on text data

I have a large raw dataset on crime and I want to cluster the data using k-means, However, I get an Error when I enter this code: ...
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1answer
867 views

Gaussian Mixture Models as a classifier?

I'm learning the GMM clustering algorithm. I don't understand how it can used as a classifier. Here are my thought: 1) GMM is an unsupervised ML algorithm. At least that's how ...
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2answers
296 views

Mixed types of data for clustering

I have the following types of data for clustering - Numeric, Categorical and Latitude Longitude data for a location in one dataframe in python. I would like to know how can I go about doing clustering ...
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2answers
27 views

How can I perform clustering on a list of words and ratings as columns?

I want to perform clustering to give words meaning like good, neutral and bad. My dataset is in the format : ...
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1answer
277 views

DONUT- Anomaly detection Algorithm ignores the relationship between sliding windows?

I'm trying to understand the paper : https://netman.aiops.org/wp-content/uploads/2018/05/PID5338621.pdf about Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection. Clustering is done ...
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1answer
94 views

Clustering time series based on monotonic similarity

Context I am involved in a task of clustering 1500 time series of 500 observations into a few number of clusters. The time series share all the same observed property at different spatial locations, ...
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0answers
48 views

How to approach edge detection using GNN on sparse data?

I'm at the beginning of a project and I really wish to try and on the way learn Graph-Neural-Networks. The goal is to find the underlying graph structure of data points, some could be more clustered ...
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2answers
41 views

Are there known techniques to transform features X classified as C to features Y classified as C'

I don't think the wording of my question is that clear myself, but I don't have any better words suitable for a title (on top of my head at least). I was wondering if given features X that is ...
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0answers
29 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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2answers
157 views

Grouping already clustered data (with a pre-defined x and y)

I have an already clustered data set (I wanna keep my x and y), where there's clearly a small group of elements in the middle that don't follow the expected patterns. I can select them manually, but ...
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1answer
127 views

clustering with heterogeneous (quantitative and qualitative)data?

I'm a Phd student and I have the results of some approaches (algorithms) that I would like to analyze. Data (results) are stored in csv files as follows: - the lines describe each algorithm with its ...
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1answer
62 views

clustering with k means

I have dataset with two label class (good and bad), I want to apply K Means on my dataset using python, should I use that label dataset or I have to delete the label class column ?
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1answer
144 views

KShape cluster centers offset?

I am using tslearn KShape to cluster time series data. I am generally happy with the results, as upon inspection, the clusters seem to make sense because of the similarity in shape and magnitude. I ...
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2answers
64 views

Similarity measure before and after dimensionality reduction or clustering

I have a dataset with 500 000 samples, each sample contains 30 features. The values of the features are in the range 0.0 to 1.0. ...
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0answers
49 views

Sliding cross-correlation [closed]

I want to understand Timeseries shape similarity algorithm ( Shape-based distance aka SBD). I can't understand the statistics behind it and why it is better than DTW or other similarity measure. I'm ...
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2answers
1k views

K-Means clustering - What to do if a cluster has 0 elements?

I'm writing code for k-means clustering. I have around 100000 vectors of size 128x1 (SIFT descriptors). I'm trying different initialization methods such as Forgy and Random Partition. What if suppose, ...
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1answer
329 views

Should unique vectors (SIFT descriptors) be used in K-Means Clustering?

I'm doing image classification by extracting SIFT features, clustering them and then finding BOVW histogram and classifying. I have around 180 training images from which I'm extracting SIFT ...
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1answer
270 views

Fuzzy Clustering for Categorical Data

I have a dataset in which each feature is either 0 or 1 (like BBOW). I want to cluster the data such that one point can belong to more than one cluster(soft assignment). I searched about this and I ...
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1answer
316 views

Find all potential similar documents out of a list of documents using clustering [closed]

I'm working with the quora question pairs csv file which I loaded into a pd dataframe and isolated the qid and question so my questions are in this form : ...
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2answers
58 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 ...
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0answers
107 views

how to extract the Top contributing labels/words in universal-sentence-encoder-large - TransformerModel?

I'm using the universal-sentence-encoder-large (Transformer Model) encoding process for embedding and then using the embedding for Clustering - Basically for unsupervised learning. I want to get the ...

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