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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159 views

Is Minimax Linkage a Lance-Williams hierarchical clustering?

I found the following article on "Hierarchical Clustering With Prototypes via Minimax Linkage". It is stated in Property 6 that Minimax linkage cannot be written using Lance–Williams updates. A ...
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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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Robustness of ML Model in question

While trying to emulate a ML model similar to the one described in this paper, I seemed to eventually get good clustering results on some sample data after a bit of tweaking. By "good" results, I mean ...
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Fixed-radius range search in non-Euclidean space

I'm trying to find an indexing data structure most suitable for my metric space: set of IP network related data (IP addresses, ports, TCP flags, ...), distance function is continuous, non-Euclidean ...
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42 views

What are the data preprocessing steps required before running K-Modes?

I have a clustering task at hand. The data that I have contains only categorical variables. So, k-modes seemed like the best option. But I am not sure what are the data pre processing steps required ...
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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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677 views

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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2answers
37 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
81 views

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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1answer
23 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 ...
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2answers
82 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 ...
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36 views

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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1answer
219 views

Anomaly detection using clustering of highly correlated Categorical data

My data has two columns and both are highly correlated e.g. if column1 has value ABC, column2 should be XYZ i.e. ABC-->XYZ. If column2 has anything else its Anomaly. Likewise there are thousands of ...
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504 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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157 views

Clustering with Replicator Neural Network

I'm trying to cluster an unknown set of data with a replicator neural network. The number of clusters is determined by the number of neuron units in the middle layer, multiplied by the number of steps ...
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1answer
48 views

Sentiment analysis of tweets (Train model on a labelled dataset and use on some other unlabelled data)

I have a huge amount of tweets on a particular topic say 'ABC' and the data is not labelled. I want to perform multi-class sentiment analysis of these tweets. I tried many unsupervised clustering ...
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1answer
29 views

Text classification based on n-grams and similarity

I have tried to cluster hundred texts using k-means clustering. I would like to consider other algorithms to group text based on their content and try to spot news not related to other news (topic ...
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60 views

K-Means Clustering Profile Plot & Data Normalization

I am new to k-means clustering and I am working on a project on cryptoanalysis. I have a few questions and I hope to get some help here. I have four variables and my variables data values can range ...
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1answer
176 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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57 views

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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2answers
55 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 (...
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32 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 ...
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1answer
27 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 ...
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1answer
21 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... <...
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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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2answers
89 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 ...
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1answer
34 views

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. ...
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1answer
28 views

Sensorfusion: Generate virtual sensor based on analysis of sensorsdata

I have a steam engine which is equipped with the following sensors: temperature sensor in the boiler room temperature sensor in the heating room pressure sensor in the boiler room rotations-per-...
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2answers
558 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
122 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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31 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
170 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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111 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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2answers
109 views

A suitable feature vector for images

I have a set of images of various products from different websites. I want to cluster the images based on the product shown in the image. How can I generate a suitable feature vector for an image for ...
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1answer
35 views

Identifying documents similar to specific clusters

Through performing clustering on a set of 1,000,000 text documents, I have identified 100 clusters. I am particularly interested in, say, 10 of the clusters. Imagine, I now have an additional set of ...
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138 views

Clustering credit card accounts based on their balance trajectories

I am trying to cluster credit accounts based on the shape of their balance trajectories over the next 36 months, to identify the different types of shapes possible in the portfolio. Here is how I am ...
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27 views

Computing spectral gap of p-laplacian, p > 2

I'm looking for code allowing computation of the spectral gap of a graph p-laplacian with p > 2, i.e. the second largest eigenvalue. See http://www.ml.uni-saarland.de/code/pSpectralClustering/...
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158 views

Clustering events in a sequence.

I've a sequence of recurring events I want to group together into representing different operation activities of the underlying process. 1) These events might potentially have an order in their ...
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4answers
142 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 / ...
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1answer
123 views
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267 views

k- means clustering on Markov chain trasition probability

I have data set of 50 students. I want to cluster them on their sequential data ( While doing a job they followed multiple sequences A, B, c total 7 stages). I am planning to apply k-means clustering ...
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590 views

How do I choose number of clusters when Eigengap heuristic suggest 1 for spectral clustering?

Eigengap heuristic Method suggest number of clusters k is usually given by the value of k that maximizes the eigengap (difference between consecutive eigenvalues). I plotted the Eigenvalue ...
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t-SNE plotting DBSCAN clustering results very scattered issue

We are trying a DBSCAN clustering model on our 30,000 samples with 15 features each. We tuned the epsilon parameter small enough to make sure the radius of the clustering circle is small while it does ...
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1k views

How to choose the optimal k in k-protoypes?

To analyze a dataset from banking I have both numerical and categorical values. I transform them to analyze with k-prototypes. The original dataset: The modified dataset: E.g.: Job (for 1 to 12 '...
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621 views

Mixed geospatial and categorical clustering

I'm working on a project that seeks to identify clusters in urban development based on location (in lat/lon) and a categorical variable (what the particular site is zoned for). Ideally, the analysis ...
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469 views

Implement gaussian mixture model with stochastic variational inference

I am trying to implement Gaussian Mixture model with stochastic variational inference, following this paper. This is the pgm of Gaussian Mixture. According to the paper, the full algorithm of ...
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0answers
2k views

Grid Search on Unsupervised Sklearn Clustering?

I am trying to use clustering algorithms in sklearn and am using Silhouette score with cosine similarity as a metric to compare different algorithms. My question is due to the varying hyperparameters ...
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518 views

graph database and its clustering

An undirected graph represents a database where nodes of the graph represent tables, edges represent the joiner columns. There are 100 databases( it means, 100 undirected graphs). We have to build ...
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79 views

Accept any suggestion to create training data from correlation matrix to find odd one out to identify difference in variation

I have N time varying feature vectors obtained by recording different parameters over time.This results in N*N similarity matrix which contains one to one correlations value for each feature. We need ...
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91 views

Selecting the number of hashes for minhash? Working with extremely sparse data and want more collisions

I'm attempting to use minhash to generate clusters and similarities, and I am primarily using ideas from these resources. http://www2007.org/papers/paper570.pdf https://chrisjmccormick.wordpress.com/...

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