Skip to main content

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.

Filter by
Sorted by
Tagged with
0 votes
0 answers
7 views

User Behaviour Anomaly Detection

I´m trying to detect anomalies in the behaviour of users in an app. My dataset have several fields, but I think the most important ones are User Id, TimeStamp, and Event_name. So for example I could ...
Juan Pablo Pereira's user avatar
1 vote
0 answers
48 views
+100

Using UMAP on text data (euclidean distance on jaccard distance matrix)

I am checking the capabilities of the UMAP dimensionality reduction algorithm, I am not sure whether the approach I am using is valid and does not violate the rules/limitations of this algorithm. ...
rkabuk's user avatar
  • 21
0 votes
0 answers
8 views

How to organize multi-layer data in Orange Data Mining?

I have data in the form of a MATLAB cell array in which: Rows are individual ROIs columns are image channels But each element of each column stores not only the mean intensity value of the ROI, but ...
DopeOmics's user avatar
1 vote
1 answer
13 views

Visualizing clusters of 3D images / volumes

I have a dataset of 3D (medical) images / volumes, think CT/MRI recordings. In other words, each of my samples consists of a 3D array of grayscale values, typically in the range of e.g. 1000x1000x500. ...
Eike P.'s user avatar
  • 111
0 votes
0 answers
10 views

Need to compare results using Ward's method

So I create clusters like this and StandardScale them ...
Poyo's user avatar
  • 1
0 votes
1 answer
17 views

Group/cluster semantically similar classes in reports?

I'm fine-tuning BERT models to binary classify reports. For example, a report can be about 'birds' or not about 'birds'. This works really well, but now I want to do multi-label classification, ...
Rob Audenaerde's user avatar
0 votes
0 answers
12 views

How to classify/recognize postage stamp varieties?

As a hobbiest stamp collector, I often run into the need for classifying stamps based on minute differences, such as these: Now, I literally have thousands of them (in ziploc bags) and I am planning ...
René Becker's user avatar
0 votes
0 answers
12 views

Best value of K when using K-Nearest Neighbors with Spectral Clustering

I'm using scikit-learn's SpectralClustering class, which has the option of building its affinity matrix using a K-Nearest Neighbors algorithm. Is there any way to ...
Hippopotoman's user avatar
0 votes
0 answers
18 views

Content based filtering with dissimilar user interests

I'm prototyping a system for recommending articles to users. A single embedding vector is generated for the article summary, and users self report a list of non-enumerated interests. Concretely, a ...
stevester94's user avatar
0 votes
0 answers
29 views

Classifying Players as winners or losers

I have a dataset that I curated from a game that I play. There are currently 130 instances (i.e. players) and an innumerable number of features. Experience tells me <10 features would be sufficient....
Shawn's user avatar
  • 33
0 votes
0 answers
8 views

Model for k means clustering of last mile logsitics data set

I want to utilize Orange for K means clustering for Network Design for Courier Company using K means clustering. Data set includes Longitude & latitude of delivery points, parcel weight, area type ...
asdcxbx's user avatar
0 votes
0 answers
4 views

How do I determine syndicate and collusion indication with clustering and network analysis on a large unlabeled user transaction data?

How do I determine syndicate and collusion indication with clustering and network analysis on a large unlabeled user transaction data? So far, I've only been training with labeled data on fraud-...
user161454's user avatar
0 votes
0 answers
6 views

Unsupervised Post-Clustering Feature Selection - Laplacian Score

I know that feature selection methods such as the Laplacian score or Fisher score are typically used for dimension reduction prior to clustering, but is there any reason why the same methods couldn't ...
EB3112's user avatar
  • 203
1 vote
0 answers
10 views

Finding parameters which reveal clustering in t-SNE

These data are from SAMHSA, Mental Health Client-Level Data. I am trying to find the right parameters to obtain clustering as in this paper. Code here. For now, I'm dropping columns which aren't ...
Jackson Walters's user avatar
0 votes
0 answers
13 views

What is normalized winning frequency in kernel self organizing map(SOM)?

In the k-means based kernel SOM, proposed by MacDonald and Fyfe (2000), the update of the mean is based on a soft learning algorithm ...
Anshuman Jayaprakash's user avatar
0 votes
0 answers
15 views

How to use pairwise data to cluster users into group?

I have a table_1 shows the phone call history, with two columns 'ID_1' and 'ID_2' (id1 calls id2), how can I find the clusters where each user in the cluster in the calls with at least 2 other people ...
kumalu's user avatar
  • 1
0 votes
0 answers
38 views

Quantifying heat map data

I have heatmap data (the x and y axis take in angles as inputs, and the color of that particular spot on the grid represents the "divergence.") I am unsure on how to numerically describe my ...
MaximeJaccon's user avatar
0 votes
1 answer
22 views

Why does largest eigen value used for eigen vector calcuation?

Why is the largest eigenvalue used for eigenvector calculation? What is the significance of using a largest absolute value for the whitening of a picture?
EMKAY's user avatar
  • 105
0 votes
0 answers
10 views

unsupervised clustering followed by modeling each cluster to create a mixed model

I am curious if this is an advisable approach. I am not applying this approach and am only interested in the theory of it. let's say you have some set of features X and target Y. X can account for ...
Phillip Maire's user avatar
0 votes
0 answers
20 views

Cluster size distribution

In unsupervized matrix clustering (i.e., no images, no graphs), has anyone ever noticed an issue with clustering algorithms where often the clusters are extremely imbalanced, to the point of the ...
Yugen Omer Korat's user avatar
0 votes
1 answer
35 views

Enhance clustering with evaluation function

My goal is to partition a dataset (X) in distinct clusters. I'm using k-means to be able to pick the center of each cluster assuming all other datapoints behave the ...
acocado's user avatar
0 votes
0 answers
14 views

Clustering of medical procedures from claims data

Imagine quite extensive medical claims dataset, containing patient related claims with medical diagnosis and procedure. While the diagnoses are neatly classified according to IDC, the procedures are ...
klobaska soslaninou's user avatar
0 votes
0 answers
9 views

Clustering for language dialects

I have a codebase in a programming language, various projects written by different people. The language is quite complex, so people use it in different styles, partially based on personal preference ...
Gergely's user avatar
  • 101
0 votes
0 answers
8 views

Unsupervised learning with bags of words with a word metric

I would like to perform clustering on a collection of documents with the assumption that I have a metric $\rho$ which tells me how close two words are to being synonyms. If $\mathcal{W}$ is our ...
jwhite's user avatar
  • 101
0 votes
0 answers
20 views

Choosing a cluster validation measure for graph clustering algorithm

I am currently solving a clustering problem. Objects to be clustered are represented as sparse vectors in R^N, N=10. The number of objects is about 1kk. To cluster, I build a graph keeping the largest ...
Sergey Tkachenko's user avatar
0 votes
0 answers
16 views

Number of cluster similarities

As I know, for a fixed set of $ N $ documents there are up to $ N^2 $ distinct similarities between clusters in single-link and complete-link clustering. In which way can I define how many distinct ...
Artem Tartakovskiy's user avatar
1 vote
1 answer
12 views

Tools for manual disambiguation/editing of match results

We are using a clustering approach to find data that is present in multiple datasets. Eg, if a product is sold on Barnes & Noble and Amazon, we want to know that it's the same product. The ...
user158890's user avatar
0 votes
1 answer
16 views

Standardization of data post-clustering

I've applied K-Means clustering to my standardized data, including columns like age and salary. I have obtained the necessary coefficients. Now, when determining the cluster for a new data point, I ...
Anand Raj's user avatar
0 votes
0 answers
20 views

How can I quantity feature importance while performing unsupervised clustering with mixed data types?

I wanted to cluster data points into 2 clusters, I am using clustmix package from R I wanted to understand importance of each of the feature, I have 203 features. I have tried featureimp package from ...
ashish pople's user avatar
1 vote
1 answer
21 views

Topic modeling evaluation

I'm working on topic modeling and I have generated clusters with two different methods. How can I evaluate which method performs better than the other?
user5520049's user avatar
0 votes
0 answers
19 views

clustering based on multiple attributes (preferably using Java)

Hello and thank you in advance :) For context, i've been willing to fiddle with machine learning for a while and think i might have found a decent use case to start : I have a set of objects Oi each ...
thomasHeidmann's user avatar
2 votes
0 answers
48 views

Discretization of Multiple Time Series

I'm working on discretizing multiple time series for a project. Here's what I've done so far: I concatenated the train signals like this: [1,2,3,5] and [7,3,6,7] into [1,2,3,5,7,3,6,7]. Then, I ...
Nathaldien's user avatar
0 votes
0 answers
20 views

Alternatives to Model-Based Feature Selection for Unsupervised Clustering

I am running a clustering model on a group of patients who are hypertensive with hopes of identifying different variations in clinical characteristics among hypertensive individuals. One of the issues ...
Zory Dory's user avatar
0 votes
0 answers
8 views

Best way to encode a tag column for clustering

I have a dataset which tells me a tech support case used a particular tech document. Every case has been tagged with which product it pertains to. Similarly tech documents are tagged with certain key ...
haldar55's user avatar
0 votes
1 answer
42 views

Visualizing Author Topic Similarities: t-SNE and Cluster Labeling

I am working on a dataframe containing abstracts from various NLP conferences, along with information on information on the respective authors (names) and the keywords they've associated with their ...
Marrluxia's user avatar
0 votes
0 answers
12 views

How to solve classification problem that we should cluster elements, with Multinomial classification from CS229?

I just learned about Multinomial classification (CS229 Lecture note (What I learned is on page 24)) and I attempted to solve a problem that Obesity classification from Kaggle. Kaggle Link I tried to ...
Gosu Choi's user avatar
0 votes
0 answers
9 views

Efficiently cluster Gaussian vectors

Let $X^i,1\leq i\leq n$ iid Gaussian vectors of $\mathbb{R}^p$. I want to cluster the coordinates $1\leq j\leq p$ knowing the following facts: Each coordinate $X^i_j$ is centred and has variance $1$, ...
kaleidoscop's user avatar
0 votes
0 answers
13 views

Clustering high-dimensional data

Every sample in my dataset consists of two components: its $x$ component is a $n\times m$ matrix, and its $y$ component is a $p\times q$ matrix (Feature selection and PCA have been done before). Apart ...
yuxuan-z's user avatar
0 votes
0 answers
8 views

Confusion about dendrogram

Consider the following dendrogram in aglomerative clustering : The distance of merging the cluster containing the points p1 , p2 and p3 with the cluster containing the points p4, p5 and p6 is this ...
John adams's user avatar
1 vote
0 answers
33 views

Looking for a way to train a model to learn parameters for clustering

I have 5000 docs, each is a review. For each review, i'm plotting the sentences in a semantic dimension. Now, I'm applying clustering to these points for each review. The success of my model depends ...
Prithvi's user avatar
  • 33
1 vote
1 answer
65 views

Beginner basic clustering model and one-hot encoding?

I have a dataframe of natural disaster incidents in Afghanistan from 2016 - 2023. Column names: REGION (Northern, Eastern etc) PROV_CODE (province) PROV_NAME DIST_CODE (district) DIST_NAME INC_DATE (...
Mas's user avatar
  • 55
0 votes
1 answer
47 views

Beginner clustering project, what are the input features and how do I analyze the data?

I am a beginner to data science. I have this dataset on natural disaster events in Afghanistan from 2016 - 2017. Columns: REGION (ex. North, North West, etc) PROVINCE_NAME (kind of like US 50 states) ...
Mas's user avatar
  • 55
2 votes
2 answers
114 views

How to deal with high data volumes? (Tools, techniques, concepts, etc.)

I have some doubts about how to deal with high volumes of data. I'm currently working in the data analysis/data science field, so I've had the chance to perform calculations, manipulate data, and ...
tms's user avatar
  • 21
0 votes
0 answers
18 views

Clustering of two datasets in different years

I want to analyze two datasets by running a clustering algorithm on both and comparing the results. The two datasets have the same variables. The only difference is that one dataset is from 2010 and ...
Ahmad Bhatti's user avatar
0 votes
1 answer
201 views

Outlier filtering from time series data

I have time series data that I eventually want to cluster after using dimensionality reduction. I am thinking about how to handle outliers. The data has seasonal/periodic patterns. I have tried IQR ...
Jim A's user avatar
  • 1
0 votes
1 answer
25 views

Encoding soft clustering results as features

I want to use cluster numbers from soft clustering algorithm output as a some sort of categorical feature (or features), add them to other features for further training in another model (Y's from soft ...
franz-german's user avatar
0 votes
0 answers
4 views

Clustering or Finding Similarities Among Portfolio Allocations

I am trying to cluster a set of portfolios with percentage allocations among Stock, Bond, Other, and Cash. I am not sure what's the best way to go about this because the variables are interdependent ...
Ahmad Bhatti's user avatar
0 votes
0 answers
82 views

Identifying recurring transaction clusters (subscriptions) at a user level

I need some help converting this issue into a machine learning problem. Goal: Grouping credit charges into clusters of recurring transactions per user Input data: List of credit card charges with <...
Fintech Pikachu's user avatar
0 votes
0 answers
60 views

divide a large group of people into subgroups based on two parameter

note in advance: I'm new to data-analysis and although my major civil engineering taught me about statistics, I did not apply it the way I would have encountered in real life, or in this field of work....
Abdulrahman Sheikho's user avatar
0 votes
0 answers
36 views

Does clustering belong to the domain of data mining or to the domain of machine learning?

Question 1. Does clustering belong to the domain of data mining or to the domain of machine learning? Or to both domains? Question 2. Depending on the answer to Question 1, could you please suggest a ...
Ommo's user avatar
  • 103

1
2 3 4 5
28