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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22 views

Dimensionality reduction without select components

I would like to use dimensionality reduction algorithm in my pipeline. I have 2k features and I'm using xgboost. My model is rebuilding each day (there are new records that should be involve to ...
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432 views

clustering 2-dimensional euclidean vectors - appropriate dissimilarity measure

I've got a set of approx. 50 000 2-dimensional euclidean vectors which are connected with 20 groups, i.e. each group has approx. 2500 2-dimensional euclidean vectors. My data includes endpoints ...
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Doubt on scope of text classification problem

I have a dataset that describes the sellers who are selling various brands. I need to identify the source (where did he buy those brands he is selling from) of those sellers. (Dimension of dataset 11,...
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70 views

Clustering with groups in data related to cluster label

I want to predict which device got used in which room. Therefore I've got device and sensor data. My idea was to create a feature vector lie this: ...
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23 views

Thematic clustering of text

Please advise on starting points, research (papers,frameworks) related to thematic clustering of text. In particular on a system with two levels of clustering where second level has a temporal nature....
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What approach other than Tf-Idf could I use for text-clustering using K-Means?

I am working on a text-clustering problem. My goal is to create clusters with similar context, similar talk. I have around 40 million posts from social media. To start with I have written clustering ...
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How to Perform Coordinate Constrained Clustering

Constrained Clustering typically refers to problems where certain pairs need to be included in or excluded from the same cluster. However the problem I'm dealing with has a constraint on where the ...
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2answers
40 views

customer segmentation with unbalanced data

I am trying to do a customer segmentation on my transactional data and I am struggling a little bit on the best approach. Since it is an unsupervised model I can throw it to any algorithm and get some ...
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Confused how to apply KMeans Clustering algorithm on my dataset to predict the difficulty level of a course

How can I apply clustering on this dataset to predict the difficulty level of a particular course in this dataset there are many student and each student has opted every course.
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1answer
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Heterogeneous clustering with text data

I have a dataset which consists of multiple user ratings. Each rating looks similarly to: ...
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Distance Dependent Chinese Restaurant Process v/s LDA for document clustering

I am trying to use Distance Dependent Chinese Restaurant Process (ddCRP) for clustering documents. Specifically, clustering users in a search log (AOL log). Each user is a document with his queries ...
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Identify and correct mislabeled categorical data in supervised learning

I have game/player level football data in 230 dimensions and want to classify the likely position that each player was playing in each match. The data is labelled, however each player is classified ...
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1answer
16 views

Clustering without information about identifier

I have a data-set with different products and binary value if it was sold in a store or not. I looks like: ...
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Assigning points to fitted planes

I’m working on a project involving fitting planes to 3D point clouds. The actual plane fitting part is working fine, but I’m trying to decide the best way to actually bound the fitted planes by the ...
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1answer
141 views

Customer Segmentation and Category association

I have to solve two questions on the following dataset: 1. arrange customers into mutually exclusive groups.explain the clusters. 2.identify 1-1 product category association rules for each cluster, i....
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1answer
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What is the difference between biclustering and clustering?

After reading the wiki page for biclustering (https://en.m.wikipedia.org/wiki/Biclustering), I am really confused on what is the difference between biclustering and clustering? Any explanation/...
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Dimension reduction using non-linear PCA

I am working on an undergraduate astronomy research in which we are analyzing geometrical complexities of different sattelite images of man-made and natural structures on Earth. The different images ...
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1answer
19 views

Hot Encode vs Binary Encoding for Binary attribute when clustering

I am planning to use data for a clustering problem that contains a column with a binary value BUY/SELL. Should I be converting this attribute and assign it binary values (BUY=1, SELL=0), and keep it ...
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1answer
20 views

Recommendation needed for unsupervised clustering on mixed data task [closed]

I have a task to perform unsupervised cluster analysis on mixed datatypes: images, physical and business measures – continuous and categorical. Businesswise: there are images of products and ...
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1answer
33 views

Binary Classification of a ship Dataset

I want to create a ship detection classifier from a dataset that is formed by 4000 photos(3072*2048).Basically i want to classify the dataset to ship-image and no-ship. I am thinking of 2 solutions- ...
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1answer
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Clustering after PCA: Use the standardized data, or take into account the variation explained at each PC?

I am interested in clustering daily gridded data. Because of the many dimensions (gridpoints), I first perform PCA to reduce the dimensionality and keep the n-first PCs that account for at least 85% ...
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1answer
28 views

How to improve results for clustering of words

I have a list of words (names actually) on which I would like to apply some entity resolution. My first guess was to create clusters of similar names so I could extract a representative entity from ...
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T-SNE good clustering but SVM classification poor

I am trying to classify in 4 different classes, paragraph embedding vector computed with doc2vec using an non-linear svm over them. When I visualize the embeddings using tensorboard t-sne I can see ...
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1answer
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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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2answers
42 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
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Determining number of clusters in high dimensions

I am doing KMeans clustering for sentence embeddings and my problem is the number of clusters. In general, feature size is an order of a few hundreds (in this case 768) and my concern is the sparsity ...
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1answer
43 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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1answer
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Clustering cartesian coordinates associated with 1 categorical feature

I have a series of 2D coordinates X = {x, y}. Each are associated with one categorical variable W that can take 7 different values. E.g: ...
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Item position in Gravity Search Algorithm

According to this article titled Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm This ...
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Average n 2D clusters into one finale result

I have n clusters run on 3D data, resulting n 2D clusters, I couldn't run the clustering model on a one year satellite mages time-serie. So I did chunks of one month and run clustering which finally ...
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2answers
33 views

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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0answers
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End to end k-means clustering - python

i'd like to share with you my path in a clustering exercise (via K-means using python), in order to understand if i made some errors or if there is something more that can i do. General Overview My ...
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2answers
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Clustering or classifing n-gram-based text categories

I have large set of data records looking like this: "text", "category" I extract n-grams from text (2-, 3- and 4-grams) and store count of each n-gram per ...
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1answer
17 views

Clustering a variable based on another variable or set of variables

df11[['COMPONENT_ID','FIRMWARE','SERIAL','CRP0_VDDN']].head() Consider I have these four columns to analyse. I want to form say 3-5 clusters of COMPONENT_IDs with ...
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1answer
99 views

Stationary time series for clustering algorithms

I have a set of time series data that I would like to feed into a clustering algorithm (like k-means, using dynamic time warping as the distance function). After standardizing the data with mean 0 and ...
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1answer
30 views

Clustering with Only Categorical Features

I am trying to do clustering with a bunch (24) of categorical features. I have done some research and found a lot of people recommending something such as K-Modes. I tried running K-Modes on my data ...
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1answer
67 views

Clustering with custom criterion (minimum cluster weight)

Edit: following comment from @anony-mousse, I'm changing the question to search for a general clustering approach that matches this criterion (minimum weight per cluster). I am to use a clustering ...
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1answer
98 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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1answer
145 views

Clustering a set of vectors

Provided a set ($m$ no. of) of n-dimensional vectors what would be the correct unsupervised approach to cluster them? The vectors essentially represent patterns. For example: Set of vector is ...
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2answers
21 views

Pre-processing mixed data prior to clustering

I am new to hierarchical clustering, and wish to perform clustering on mixed data. I am slightly confused on the necessary pre-processing steps. I understand how to pre-process purely continuous data, ...
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0answers
16 views

Clustering a dataset and creating a model per each cluster

I was wondering if it makes sense to cluster a dataset to find closely related data points and train a binary classification model for each of this clusters as they would be minidatasets. I'll ...
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1answer
21 views

I want to know which machine learning algorithms can be used for trajectory classifications?

I am working on project for clustering of air objects based on their trajectories. Like I want to train a model on dataset of different flying object's trajectories so later I can predict what type of ...
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2answers
33 views

How to find vertical clusters in 1-D data

I have residuals of a multivariate time series data obtained from sensors on a server.spikes in the plots of residuals indicate abnormal server state. I want to cluster the data into vertical clusters ...
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1answer
28 views

Which approach to select category based on keywords

I want to assign a certain category to a group of keywords. So i.e. people can upload images or videos, when they do this they can set keywords for this. These keywords are free to type so words can ...
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1answer
26 views

How to identify new clusters that the training data has never seen

I have to identify the different operational states of a server. I have readings related to the different sensors of the server ( like temp sensor,fan speed sensor,job load sensor etc).The data I have ...
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1answer
22 views

Trying to understand the Fowlkes-Mallows Score

I recently bought Chris Albon's ML flashcards and I'm working my way through them. But this one on the Fowlkes-Mallows score has me stumped, as his definitions of false negatives and false positives ...
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2answers
103 views

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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1answer
30 views

How to tune parameters batch by batch?

As the title states, I am trying to cluster a huge dataset and cluster it by using sklearn.Birch to learn incrementally. If it's a small dataset, I could just use ...
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4answers
186 views

K-modes clustering: Estimating which features were most impactful on clustering?

I have entirely categorical data (survey results from users), so I've used k-modes clustering to better understand my users. I'm not an expert at clustering methods at all. Is there a way to known ...
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2answers
162 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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