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.

195 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
6
votes
0answers
103 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 ...
5
votes
2answers
2k views

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 ...
4
votes
2answers
27 views

Ordering scrambled 1D data sets by continuity

This is a cute little clustering problem that was probably solved a million times over, but I couldn't find a good reference for it. I have 20 1D datasets with 400 entries each. In the picture they ...
4
votes
2answers
8k views

Multivariate Time-Series Clustering

I have a streaming data along with timestamp dataset that looks like this: 1.png Timestamp can be inclusive of "seconds" too, but the data may or may not change every second. it depends on the ...
4
votes
0answers
278 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 ...
4
votes
0answers
84 views

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 ...
4
votes
1answer
302 views

Clustering efficiency in a discrete time-series

Is it possible to identify the point in time where the cluster separation is at its most in a discrete time series clustering? Say I have 4 clusters of discrete time series and I want to pick a ...
3
votes
0answers
59 views

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 ...
3
votes
0answers
142 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 ...
2
votes
1answer
37 views

Clustering of Weekday Weekend Time Series Data

I have a dataset of the number of steps people take throughout a day over a period of months. I aggregated them so that each person will have an average weekday and weekend time series of steps. An ...
2
votes
2answers
37 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 ...
2
votes
1answer
29 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 ...
2
votes
1answer
52 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, ...
2
votes
0answers
22 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 ...
2
votes
2answers
58 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 ...
2
votes
0answers
70 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 ...
2
votes
0answers
88 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 ...
2
votes
0answers
14 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/...
2
votes
1answer
175 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 ...
2
votes
3answers
198 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 ...
2
votes
0answers
145 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 ...
2
votes
1answer
102 views

What is the name of this similarity distance metric?

...
2
votes
2answers
655 views

Clustering with multiple distance measures

I'm trying to use clustering to automate a group-finding process with the aim of being able to automatically detect groups in unseen data. The data are html elements within any given webpage, this ...
2
votes
0answers
390 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 ...
2
votes
0answers
905 views

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 ...
2
votes
0answers
793 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 '...
2
votes
0answers
520 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 ...
2
votes
0answers
432 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 ...
2
votes
3answers
321 views

Clustering big data by reducing data accuracy?

I have 1 million rows with 20 attributes to do hierarchical clustering. When I want to build a distance matrix on this data by dist() in R, it says that it needs 5 ...
2
votes
0answers
410 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 ...
2
votes
0answers
76 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 ...
2
votes
0answers
82 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/...
2
votes
0answers
59 views

Finding dominating attributes with in the clusters generated

I am having a dataset of customers where each customer is represented as some feature vector and I am applying K-means algorithm to this dataset. On the basis of those features, I can abstract and ...
2
votes
0answers
32 views

How to apply K-Medoids in many CFG?

I am having around 1000 DAG(Directed Acyclic Graph) of different files showing java.io.BufferedReader usage. Following is representation of one of the graphs ...
2
votes
0answers
60 views

Spatial clustering of data points on a grid to obtain variable resolution map with constant statistical confidence

I have a grey valued image which is calculated as the mean of a series of images. The value of each pixel is therefore associated to a standard error. The pixel values and the relative standard error ...
2
votes
0answers
43 views

Discovering dis-associations between periods of time-series

I'm interested in discovering some kind of dis-associations between the periods of a time series based on its data, e.g., find some (unknown number of) periods where the data is not similar with the ...
1
vote
0answers
32 views

Different approaches for categorical non-ordered data clustering in R

I'm trying to find different clustering approaches for only categorical data in R, so far I found: klaR for kmode cba for rock Hierarchical clustering (agglomerative or divisive) with a categorical ...
1
vote
1answer
15 views

ML Approach for Getting List of Observations with Similar Features (Discrete+Continuous)

I have a dataset with 19k observations. Each has approximately 448 features: - Text description turned into vectors of size 300 - 16 categorical variables represented numerically - The remainder ...
1
vote
1answer
28 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 ...
1
vote
1answer
17 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 ...
1
vote
2answers
20 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 ...
1
vote
2answers
35 views

Clustering on imbalanced data that has high correlation

I am clustering images of two categories, but for the purposes of the experiment, I do not know the labels i.e. this is an unsupervised problem. Via ...
1
vote
1answer
24 views

Tool for clustering and cleansing data set

I have a large-ish data set (400K records) composed of two fields (both strings). I am looking for a tool that will enable me to cluster the data e.g. around the first column, either using exact ...
1
vote
1answer
16 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-...
1
vote
1answer
53 views

Machine learning tasks classification

I am trying to be precise in definitions. We can solve regression, classification, clusterisation, dimensionality reduction, visualization, feature extraction tasks. But also there are supervised, ...
1
vote
0answers
143 views

“Memory Error” - Kmeans in python using pandas DataFrame

I am trying to predict on my "dataset_to_predict" having size of (297000 x 5120). While Memory usage is under 50%. No Specific Error message. I'm trying to find # of k using elbow method - Got ...
1
vote
0answers
22 views

Can I use entropy as a measure for determining significant variables in a cluster after clustering?

After clustering my data into k groups, I would like to determine for each of the clusters, which dimensions(variables) significantly describe that particular cluster. For example, lets say cluster A ...
1
vote
0answers
46 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 ...
1
vote
1answer
41 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 ...
1
vote
0answers
60 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 ...