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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6 views

Cluster evolution over time

I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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Rank links from rss feed

I am trying to create a script to filter the most "intersting" articles from an rss feed and rank them. ...
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How to plot datasets 1 factors for K mean clustering python

I'm unable to plot the data for K mean clusering algo usingsklearn as it throws this error : TypeError: scatter() missing 1 required positional argument: 'y' Here is the function I have written to ...
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33 views

Apply a clustering algorithm on categorical data with features of multiple values [duplicate]

Let us I have a people data like gender, age, marital status, education, employment, hobbies. I want to make clusters of those people, having some similarity/common among them (for example they have ...
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Question about Similarity vs Dissimilarity Matrix

Right now, I'm working on a coming up with a similarity vs dissimilarity matrix for a set of data points for a clustering algorithm. My question is, if I want to use one of the many clustering ...
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Question About Coming Up With Own Function for Distance Matrix (For Clustering)

Right now, I am currently working on implementing a clustering algorithm with millions data entries with regards to game users for a mobile game. A lot of the features I plan on using are unique to ...
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1answer
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Scaling of ordinal data before both hierarchical and KMeans clustering

I am new to data analytics. As part of my assignment I have to perform both hierarchical and Kmeans clustering on a data set wherein all applicable variables are ordinal (1-5 rating scale). Do I need ...
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13 views

Clustering in python when imbalanced data sets exist

I have a set of measurements with four features. Two features are continuous (time and distance) and two are discrete. We also know that the population consists of two groups. One is the minority ...
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How to tune / choose the preference parameter of AffinityPropagation?

I have large dictionary of "pairwise similarity matrixes" that would look like the following: similarity['group1']: ...
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1answer
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Clustering stores based on weekly data

I have 1 year transaction level data aggregated at a weekly level for 1000 different stores. I want to cluster similar stores based on 8 variables such as sales, customer count etc. The concern is ...
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Setting up Zeppelin to work with a Spark Cluster

I have made a spark cluster containing 2 workers and 1 master. I followed the following link to set up the spark clusters. After successfully setting up the spark clusters I wanted to connect it to ...
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1answer
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what arguments should I pass to dbscan or optic in order to divid the data in a specific way

I have thousands of very similar data set that needs to be divided in diagonal way to two groups. for example: and I tried to play with the argument of dbscan and optic as eps and minPoints and even ...
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Is there a machine learning method to rank customers credibilty (goodness of a customer)?

I am working on a machine learning project that I want to rank each customer and put on a scale smt like one of those https://cdn1.vectorstock.com/i/1000x1000/90/40/credit-score-indicators-with-color-...
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3answers
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Supervised clustering

I'm working on a clustering problem. I have a training set composed of sets of points where the clusters are known and I want to find the good clusters on a testing dataset. It's a kind of supervised ...
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How to validate a clustering model without a ground truth?

Im dealing with a dataset (text messages about source code comments) that are not labeled. I don't have a assumption about the implicits classes in this dataset. I want to discovery (by clustering) ...
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2answers
28 views

Categorical features preprocessing for clustering

Can anyone tell suggest the best practice for clustering data with mixtured features (both with categorical and continuous). I am struggling with a problem; I realized that for all metrics algorithms ...
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Clustering of very high dimensional data and large number of examples without losing info in dimensions

I'm trying to get a grasp on scalability of clustering algorithms, and have a toy example in mind. Let's say I have around a million or so songs from $50$ genres. Each song has characteristics - some ...
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How to remove noise using morphological filtering

I have two groups of dots that both contain noise between them: The line that separates the two groups in the picture is diagonal in shape. I tried to use morphological filtering on this image to ...
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How to calculate Fuzzy C-Means problem by hand

I figured that this doubt next can interest another students like me and help others also that are trying to understand mathematically the fuzzy c-means mathematical mechanism already that some books ...
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The actual results and results from pickle files are not matching in pandas for DBSCAN clustering

I've built a DBSCAN clustering model. The output result and the result after using the pickle files are not matching. Based on HD and MC column, I am clustering WT column. ...
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Clustering data by multiple values

I am trying to find a way to cluster/group students by their knowledge of different subjects. Given following as an example: ...
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1answer
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How would one get the HTML structure of a web page as a numeric vector? [closed]

Suppose you want to cluster, or classify, web pages inside a domain. In the same domain similar web pages always have the same structure more or less. (think of every product page an e-commerce would ...
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1answer
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What is an appropriate machine learning technique to analyse development of status over time? [closed]

I have a dataset as follows (not the actual data, but representative): ...
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1answer
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How to structure my data into features and targets for PCA on Big Data?

I want to apply the PCA algorithm from Scikit-Learn.(https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html ) At the part where I have to separate the features and the ...
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1answer
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Are DBSCAN and dbscan from the sklearn.cluster package different?

I'm new to DBSCAN. I was looking at a few examples online and came across a few instances where the following lines were used while importing the dbscan module: <...
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1answer
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Suggestion on tag clustering visualization

I have a database of tags given by users to the product. For example user; product; tag 1; A; Tag1 1; A; Tag2 2; A; Tag1 2; B; Tag1 .. .. I am trying to cluster ...
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1answer
27 views

Unsupervised Clustering high dimentional data not having estimation for K

I have a dataset (all numerical) of 50K records containing 500 features. we are trying to find fingerprints. Meaning that we would like to cluster the data and report one of the nodes in each cluster ...
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1answer
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trade offs between number of features with its score

I am running k-mean clustering on ~200000 samples. The dataset has in total 14 features. One feature is id and the rest are categorical. I have been playing with ...
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1answer
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External cluster evaluation for a varying number of cluster

There are many external clustering indices like (Adjusted) mutual information, (Adjusted) Rand index, and many more. However, they are not very good at comparing clusterings where the number of ...
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Modifying BERT sentence encodings

I'm using BERT to encode sentences. The sentences I'm encoding are quite similar, meaning they all belong to the same overall topic. Therefor, I am using another parameter for measuring similarity. ...
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18 views

Clustering of Sparse Time Series

I have a dataset of certain user activity per week (e.g. purchasing an item or using a service per week) for the past 52 weeks and for 100K+ users. The matrix is very sparse (85% of the entries are ...
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1answer
71 views

Time series clustering using dynamic time warping and agglomerative clustering

I'm new to data science and I'm currently working on a project to classify electricity consumption profiles. This consists of electricity meter readings taken from sites on a half-hourly interval ...
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1answer
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Clustering analysis for observations with lists as data

So I have several samples analyzed for their chemical composition. After data analysis, for each sample, I have a list of compounds found and their corresponding relative abundance. Some compounds are ...
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11 views

Neural network approaches for classification of time signals

I have 3D images that constitute of 2 spatial dimensions, e.g. (x,y) coordinates, with the 3rd dimension being a time signal. The signal is not periodical, but related to physical properties of medium ...
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1answer
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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,...
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1answer
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What is a color blob? Is it possible to use clustering algorithm to color blob detection problem?

Wiki gives this definition of blob detection In computer vision, blob detection methods are aimed at detecting regions in a digital image that differ in properties, such as brightness or color, ...
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How to properly use approximate_predict() with HDBSCAN clusterer for text clustering (NLP)?

I have approached text clustering using HDBSCAN based on this article which describes how to do this in R. I've re-written this in Python using this library. The approach is to first calculate TF-IDF ...
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2answers
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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 ...
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2answers
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perform cluster on a multiple dimensional data in R

I have a data set which has 2488 samples and each sample has 13 features.Now I want to perform cluster on this data set in R but I found k-means method usually for two dimensions data.So can any one ...
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1answer
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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 ...
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Is there any paper introduce an intuitive method for clustering evaluation?

I would like to use the most intuitive method like minimizing the within-cluster distance and maximizing the distance between neighbouring clusters, but not sure does this method have a name or ...
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1answer
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Image clustering with deep learning

I want to cluster image, since varibility intra and inter class of images is huge I think reducing dimensions with a convolutional autoencodeur can be a good tools. Then I apply clustering on the ...
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Clustering text documents from multiple sources

Let's say I have a set of text documents. Half of the documents are concise social media posts containing a lot of shorthand, and the other half are long news articles. Also, half of the documents ...
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1answer
40 views

Is there any method to determine which clustering algorithm to use on a particular dataset?

I'm having a hard time getting kmeans to cluster data effectively. It fails to segment data well even for a simple attribute with 5 categories. I'm aware of DBSCAN, Hierarchical Clustering and GMM. ...
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1answer
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Clustering (unsupervised learning) for uneven classes

I am looking for an unsupervised method that can see also the points that start to look different from the majority. Which clustering techniques (I use python) can be used for such data sets? I have ...
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2answers
50 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 ...
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How to compare different similarity measurements in text clustering?

I have a dataset which contains vectors generated from subtitles (each column represents a genre, each row is a movie name), my purpose is to find the most similar movie titles, I want to use ...
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DBSCAN: How does a quantile of kNN relate to the share core points?

I read this answer by Anony-Mousse to an other question related to density based clustering and how to potentially come up with an eps. It states, that if you want 90% of you points to be core points, ...
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2answers
80 views

Are there any algorithms for solid polygon clustering?

I'm looking for something like K-Means for dividing solid polygons into regions. K-Means clusters discrete points. But I want to cluster (that is, partition) the points of solid polygons. I don't see ...
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1answer
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Find shared properties of a cluster samples

I have a dataset which contains ~15 features. With the elbow method, I found out that the optimal number of clusters is probably four. Therefore, I applied the K-means algorithm with four clusters. ...