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
-1
votes
1answer
32 views

How can I categoriese / classify a cluster of words?

I am just wondering if it is possible to classify word clusters? For example if I provide you an array of words [bird,chicken,dock,park,apple,grapes,furits,juice] ...
0
votes
1answer
31 views

Clustering of users in a dataset

I have a dataset of book reviews: ...
-1
votes
1answer
171 views

No target variable in my data

I have a list of transactions of bus route from place to place, I don't have any target variable here. I was asked to give meaningful insights from the data, what can I do here? I cleaned the data, ...
0
votes
1answer
46 views

Preparing dataframe to carry k-means clustering [closed]

Im trying to apply 3 different algorithms of clustering on my dataset. to check which one fits the best. I'm confused how should I convert my dataframe -k-means -DBSCAN -hierarchical clustering ...
0
votes
0answers
111 views

sklearn & Meanshift for NLP only returns 1 cluster

I am using sklearn.clustering to work with some text data and the MeanShift algorithm. I have: Done all standard NLP data prep like lemmatizing, removing stop ...
2
votes
1answer
41 views

Conditional Entropy and Mutual Information - Clustering evaluation

First of all, I am doing clustering and I have the true labels for my data. For evaluation, I am using the weighted average of the entropy values for each predicted cluster. I also came across with ...
2
votes
1answer
39 views

how to implement a hierarchical clustering technique using parallel execution in R

In R, currently to implement wards method for hierarchical clustering, I use the following code - results <- hclust(data, "ward.D2"). However as my data size has ...
4
votes
2answers
247 views

Random-Forest-based Similarity Matrix for clustering: how does it behave?

I am in the following context: Data: static, baseline health data at the patient level, 40 features, sparse (~ 25 binary features with many 0 or many 1 + other categorical features) Objective : to ...
6
votes
3answers
324 views

How to give a higher importance to certain features in a (k-means) clustering model?

I am clustering data with numeric and categorical variables. To process the categorical variables for the cluster model, I create dummy variables. However, I feel like this results in a higher ...
0
votes
0answers
25 views

How to analyze sets within Orange

Hi coming up with similar question like Q49352 - Clustering with sets as values . I am looking for the right way to work with sets in Orange. My set has a list of workflow steps from a workflow ...
0
votes
0answers
26 views

Is the mean shift algorithm adapted to my problem?

I'm currently building a model that can detect abnormalities in a timeserie. First, we predict the next steps and then we compare the prediction with what we measure in real time. We want to see if ...
0
votes
1answer
28 views

How to cluster text-based software requirements

I'm beginner in deep learning and I'd like to cluster text-based software requirements by themes (words similarities/frequency of words) using neural networks. Is there any example/tutorial/github ...
-2
votes
1answer
54 views

Clustering for variables with large amount of categories

I have a dataset which, has variables with a lot of categories (some more than 1000). Since, large amount of categories effect the accuracy of the model. I saw some literature stating that if you do ...
0
votes
0answers
17 views

Why is my LOF algorithm producing the opposite result it should?

What could cause the local outlier factor (LOF) to output below 1.0 for outliers and above 1.0 for inliers? I have my code sort of working just by inverting the output, but I can't figure out what's ...
3
votes
2answers
6k views

Perform k-means clustering over multiple columns

I am trying to perform k-means clustering on multiple columns. My data set is composed of 4 numerical columns and 1 categorical column. I already researched previous questions but the answers are not ...
1
vote
1answer
67 views
0
votes
0answers
310 views

How to validate clusters after calculating Gower distances and Ward's clustering in R

I am trying to apply Ward's clustering on a mixed types dataset, and wanna explain what I did (maybe helpful to others), and I have some questions regarding this analysis, mainly how to validate my ...
2
votes
2answers
64 views

How long would it take to become proficient in machine learning for someone with a non-statistical mathematical background?

I am currently a postdoc and my PhD was in applied mathematics in the area of numerical analysis and electromagnetic/acoustic wave propagation. There was no statistical element to my PhD, it was ...
0
votes
0answers
384 views

Clustering of multi-label data

The dataset consists of 1) a set of objects and 2) a set of labels, which are used to describe the objects. For the moment, for simplicity sake, each label can be marked as either true or false (...
1
vote
2answers
58 views

Is there any clustering algorithm to find longest continuous subsequences?

I have data which contains access duration of some items. Example: t0~t5 is the access time duration, 1 means the items was accessed in the time duration, 0 means it wasn't. ...
1
vote
4answers
95 views

How do I identify clusters that match on categorical data?

I am seeking some directions for a proper path to research the solve for this problem: My company made all our employees take a "StrengthFinders" test, which results in every employee being assigned ...
3
votes
1answer
37 views

How to approach clustering of time-series with a single variable

Let me preface this by saying that I'm a complete beginner to R and data science in general, so my apologies if this is a rather trivial question. I do have a rough idea of what I would like to ...
0
votes
1answer
366 views

How to run AgglomerativeClustering on a big data in python?

I run AgglomerativeClustering on a sample of data and fit a model. then I decide to predict this fit for all of my data but I got MemoryError. How can I run ...
1
vote
1answer
720 views

Building a large distance matrix

I am trying to build a distance matrix for around 600,000 locations for which I have the latitudes and longitudes. I want to use this distance matrix for agglomerative clustering. Since this is a ...
1
vote
0answers
562 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
2k views

Clustering based on distance between points [closed]

I am trying to cluster geographical locations in such a way that all the locations inside each cluster are at max within 25 miles of each other. For this, I am using Agglomerative clustering. I am ...
0
votes
2answers
244 views

Variable Importance in unsupervised anomaly detection algorithms

I am working on an anomaly detection problem to detect fraud in insurance claims. I have used the PyOD package and used algorithms like ABOD, CBLOF, Isolation Forest, and AutoEncoder. I couldn't find ...
2
votes
2answers
141 views

Is it wrong if I cluster numerical attributes and categorical attributes separately?

I have a dataset of credit customers containing mixed data types (numerical and categorical with several levels). I am trying to perform segmentation so that I can end up with k groups and then build ...
0
votes
2answers
645 views

k modes: optimal k

I have categorical data and I'm trying to implement k-modes using the GitHub package available here. I am trying to create clusters in my (large) dataset of say, 5-7 records, each of most similar ...
1
vote
1answer
122 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
1answer
175 views

How to calculate a weighted Hierarchical clustering in Orange

I am doing my first cluster analysis with Orange (which I recently discovered and looks promising for this iterative and interactive process). Apparently, there are several methods of creating ...
1
vote
2answers
324 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 ...
1
vote
1answer
37 views

Define value of a centroid

I want to choose a clustering algorithm for which I can define the value of centroids (and not only the number of them). Which algorithm should I look at? My understanding of k-means is that I can ...
1
vote
0answers
69 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 ...
3
votes
1answer
35 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
3answers
86 views

Are there algorithms for clustering objects with pairwise distances, without computing all pairwise distances?

I'm looking for a clustering algorithm that clusters objects, by using their pairwise distances, without needing to calculate all pairwise distances. Normally pairwise clustering is done like this: (...
0
votes
1answer
88 views

Clustering by Distributions of Groups of observations

I have data as shown below. Like the groups shown A,B,C.. there are roughly 5400 groups of data and 15 observations for every group (I sampled 6-7 for each group and pasted them here). ...
1
vote
0answers
33 views

Clustering and graphing similarities of sentence subjects

I have a bunch of sentences. Each sentence is given a weight of how "close" it is to a particular subject. Ex. "I love reading math books" Subjects for the above sentence = ...
-1
votes
1answer
50 views

Why in spectral clustering number of eigen vectors is same as number of clusters that we want?

In spectral clustering we take eigenvector corresponding to K smallest eigenvalues. Then we do K means clustering on these eigenvector to get final clusters. What will happen if we take different ...
0
votes
1answer
205 views

Use output of K-Mean for Logistics regression

I've created a binary classifier using K Mean, which predicts fraud and legitimate accounts, 0 and 1. This uses two features, let's say, A and B. Now, I want to use other features like C and D, to ...
1
vote
1answer
175 views

Hierarchical Clustering and Variable Selection

I am using "Single linkage" hierarchical algorithm to cluster my data points with Gower Distance as my data have both qualitative and quantitative variables. After applying this for the full ...
2
votes
3answers
1k views

Clustering algorithm for a distance matrix

I have a similarity matrix between N objects. For each N objects, I have a measure of how similar they are between each others - 0 being identical (the main diagonal) and increasing values as they get ...
0
votes
1answer
41 views

Scikit learn kmeans with custom definition of inertia?

I've coded a small clustering algorithm for time signals using kmeans, which works ok (gives acceptable results). However, kmeans uses the sum of squared differences. I would like to be able to input ...
1
vote
0answers
39 views

Anomaly detection in structured textual data

Pls refer screenshot for sample data. As can be seen most of the fields in data are textual and highly correlated but each row has unique values and hence won't be right to call it categorical. I ...
1
vote
0answers
21 views

recommend new category paths based on factor item matrix and sales of the items

Matrix A be a user item matrix. Upon performing UV decomposition, I have just the V matrix. The matrix A differs every week and I get a new V matrix every week. The matrix U is not kept track of and ...
0
votes
2answers
472 views

Anomaly detection on multidimensional time series

I have relatively little knowledge of unsupervised machine learning. I'm working on a project that aims to find anomalies in a set of n data, measured every ...
2
votes
1answer
41 views

Which algorithm or tool to use to classify as good or bad?

I have a feature vector with different data types, Considering all the data in that feature vector. I have to classify as Good or Bad. Which algorithm should be used to just get the output Good or ...
-1
votes
1answer
26 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 ...
1
vote
1answer
35 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 ...
0
votes
4answers
73 views

After running K-means on 12 features, I get an array containing clusters for each row. What is the next step after this?

So I used the elbow method to identify the optimal number of clusters, i.e. 4 in this case. After running K-means on dataset with 12 features, I get an array with the cluster number each row belongs ...