All Questions
Tagged with k-means unsupervised-learning
80 questions
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38
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What if PCA is unable to group my samples, but K-means perfectly clusters them? Is there any problem with my data analysis? Is it possible? [closed]
I am not an expert, but I am currently using unsupervised methods to better explain my mass spectrometry data obtained via DART-MS analyses. I am still learning.
It turned out that when analyzing my ...
- 11
0
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1
answer
50
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In unsupervised learning, is a result of 2 clusters meaningful?
I used both agglomerative clustering and k-means on a dataset and see the results below. Result from agglomerative clustering was demonstrated with silhouette score while kmeans with inertia score. ...
- 219
1
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1
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339
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Unsupervised learning: How to identify differences between clusters?
I'm learning about unsupervised learning and I tried to use KMeans, AgglomerativeClustering and DBSCAN on the same datase. The result was ok, they seems to work fine according silhouette_score() ...
1
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0
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48
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Can K-means put most of the noise in the same cluster?
I am working on clustering text data (very short sentences) vectorized with tf-idf. The data are characterized by high sparseness and the presence of abundant noise (considered here as documents that ...
- 11
2
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2
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3k
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Implementing cross-validation to tune the hyperparameters of an unsupervised model
I have extensively researched the application of cross validation for unsupervised learning (as it is a requirement by my project manager) but it seems that there is no clear consensus as to how to ...
- 35
2
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1
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59
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Infer limits of unscaled values from their standardized values - Clustering
I am working on a clustering problem and I have some skewed variables.
So, I log transform them and use them in clustering.
However, instead of multivariate clustering, I do multiple univariate ...
- 3,342
2
votes
1
answer
116
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Converting unsupervised to supervised problem - Overfitting - bad?
I am working on a customer segmentation using 5 features such as recency, frequency, monetary, tenure, unique_product_cnt etc.
So, I did a RFM based segmentation where I used ...
- 3,342
1
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1
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377
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standardization/normalization for 1D clustering?
I have two input variables revenue and age. Am trying to find different bins within that variables.
For ex: I have ...
- 3,342
2
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1
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5k
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silhouette score vs Distortion score
I am working on segmenting my customers with clustering. My dataset size is 7315 rows and 30 features.
So, as a beginner to clustering, I passed all my 29 features (excluding id column) to the cluster....
- 3,342
2
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1
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461
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Meaningful to retrieve original value after standardization using clustering
I already referred these posts here and here.
Currently, I am working on customer segmentation using their purchase data.
So, my data has below info for each customer
Based on the above linked posts ...
- 3,342
1
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1
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523
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RFM Customer segmentation - Why Avg monetary value instead of total monetary value?
I am trying to segment our customers based on their purchase data. And I came to know about the RFM technique (Recency, Frequency and Monetary) through these posts here, here etc.
Recency - How ...
- 3,342
3
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548
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PCA explained variance and model inertia
I'm trying to perform a PCA to reduce the dimensionality of my data and subsequently perform a K-Means algorithm.
I initially chose 4 Principal Components because they explain 70% of my variance. This,...
- 63
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84
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How to save a Higher accurate K-means Model on a unlabelled data based on Any Performance Evaluation Metrics?
I am experimenting on Iris dataset. I am not using the label. I want to save my model based on any Performance Metrics. According to Performance Metrics which model have higher score I am choosing ...
- 51
1
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1
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421
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Identity for K-Means Clustering
The property (12.18) from here states that $$\frac{1}{|C_k|} \sum_{i, i' \in C_k} \sum_{j = 1}^{p} \left(x_{ij} - x_{i'j}\right)^2 = 2 \sum_{i \in C_k} \sum_{j = 1}^{p} \left(x_{ij} - \frac{1}{|C_k|} \...
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134
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K-Means clustering technique for monthly data
I have an Unsupervised problem where user's Credit Card payment data is given for each month for various users for one year. One of the feature in the data having "User Id". For most of the ...
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0
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1k
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Using Silhouette Score to evaluate different clustering algorithm
I am trying to compare different clustering algorithms on a dataset and compare the model performance. Since the dataset is quite big (56 features), I applied PCA to reduce the number of features to ...
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1
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2k
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How to evaluate unsupervised Anomaly Detection using k-means
I'm trying out different anomaly detection models and would love to hear opinion on my idea from somebody experienced. My goal is to perform anomaly detection with different models and to give each ...
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4
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1
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905
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How to compare clustering results between "raw" and normalized data
I have a dataset and I would like to apply a clustering algorithm to find some groups. I do not have any label, so it is just wondering if I can find relevant clusters. If it may help, it is ...
- 566
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0
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31
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What the difference between clustering methods?
I am focusing on a thesis for introducing clustering methods. Chapter 3 includes hierarchical clustering, partitional clustering and density-based clustering. Meanwhile, chapter 4 is mainly on Self-...
- 111
3
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1
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4k
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KMeans clustering - can inertia increase with number of clusters
I am doing kmeans clusters on sales data and i see that inertia increases for the initial increase in the number of clusters. Can you please explain why that happens?
I am doing Batched Kmeans for the ...
- 85
1
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2
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464
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Confused between K-Means and Hierarchical Clustering for 9 different categories
I am trying to classify 9 different species of elephants into clusters using unsupervised learning. I have the following data about them:
Their height
Eye Colour
Sound they produce in decibel (dB)
I ...
- 11
2
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1
answer
124
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K means clustering breakup---galaxy spectrum data set
I have a spectrum data set (total 22000). Similar to an electronic wave data, two dimensional (Flux vs Wavelength). A typical set of wavelength plot looks like below
Now I am doing kmeans on this ...
- 171
2
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0
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57
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How can I cluster sequential data?
Suppose that I have a sequence of vectors $y_n \in \mathbb{R}^m$ for $n \in \{1, \dots, N\}$. My goal is to divide $y_n$ in $K$ clusters and want my clusters to satisfy the following conditions:
Each ...
- 286
1
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1
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352
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is k-means generalizable at any distance? [duplicate]
The classical version of k-means uses the Euclidean distance in the first step, and the arithmetic mean (the value center) in the second step. Is k-means generalizable to other distances and other ...
1
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0
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16
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Prerequisites/Checks for performing clustering
What are the checks that should be done on our data before performing clustering? Like how to check whether the dataset contains clusters of equal size/density or the clusters present in the dataset ...
- 51
3
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0
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410
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When to use K-Medoids instead of K-means
When it's better to use K-Medoids rather than K-Means? Can anybody give some examples of dataset for the same?
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1
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1
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2k
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Limitations of K-Means Clustering [duplicate]
I was going through a document of Western Michigan University to understand the limitations of K-means clustering algorithms. Below is the link:
https://cs.wmich.edu/alfuqaha/summer14/cs6530/lectures/...
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1
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1
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477
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What happens when $k=1$ in k-means? What's the optimized value of distance for $k=1$?
What is the optimized value of distance $V(x,c)$ when $k=1$ (number of clusters) in k-means? What is the centroid such that it is optimal?
$$V(x,c) = \sum_j \sum_{x_i \rightarrow c_j} D(x_i,c_j)^2$$
...
- 121
3
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2
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4k
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What is the mathematical definition of the 'Elbow Method'?
In K-means algorithm, it is recommender to pick the optimal K, according to the Elbow Method. However all the tutorials explain the elbow method in these 4 steps:
Run K-means for a range of K's
...
- 318
1
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1
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743
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Anomaly detection on high dimensional Data using k means/SVM/LOF? [closed]
I am working on one Anomaly detection problem (unsupervise problem)
Data set have
1) 15 columns and around 8k rows , including normal and abnormal(outlier ) rows, without label , all are numeric
...
- 21
1
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0
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94
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How do I evaluate a K-Means unsupervised anomaly detection approach?
how do I evaluate K-means clustering anomaly detection method as there is no labelled data of anomaly class. To find the cluster (K), I have used the silhouette score from Scikit learn library. Scikit ...
- 11
4
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2
answers
8k
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Missing data in k-means cluster model
I'm working on clustering email addresses using K-means based on their value to and engagement with the company (metrics such as % of emails opened, # of web browsing sessions, etc). I would like to ...
- 41
0
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1
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239
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Why is k correlated with the mean and variance of the distance between centroids in k-means?
I've noticed that if I'm doing k-means clustering (in MATLAB) on basically any set of data (not randomness), the mean and variance in centroid linkage distance appears to always be approximately ...
- 111
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3
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450
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How to identify or give a meaning to the cluster membership in a hierarchical clustering?
I know clustering is a type of unsupervised learning problem, however when Kmean clustering is used one can sort the membership based on the cluster centers.
For example consider the cluster ...
- 281
1
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1
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3k
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K-means classifies 96% of my data in 1 cluster. Any suggestions to improve the results?
Problem: K-Means clustering shows 96% of my data belongs to one cluster. How can I improve my results or should I conclude that no cluster exists in my dataset. Dbscan clustering shows 1 cluster ...
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1
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1
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49
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Using k-means to segment customers in the positive class
I have some labeled data (0=didn’t cancel, 1=canceled) that I am creating a model for in my marketing class.
On top of predicting who is likely to cancel, I’d like to explore the possibility of ...
- 355
1
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2
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1k
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Running k-means clustering with k = 2 recursively on clusters greater than a certain size
Does it make sense to run k-means with k (number of clusters) of 2, and then for every cluster bigger than N, run k-means again with k = 2? We can then keep doing it until we have all clusters of size ...
- 129
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1
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464
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Can I use the Silhouette to measure quality of clusters in different dimensions?
Can I use the Silhouette to measure quality of clusters in different dimensions?
For example, let's say we run kmeans for some $k$ using 6 features of the dataset. Mark the resulted silhouette as $...
- 141
0
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2
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871
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R - high dimension data using k means clustering [closed]
The dataset is 1000(observations) x 700(variables), After using pca to do dimension reduction, PC150 explained 85% Variance, so I use this (1000 x 150) data to do k means clustering.
This code was ...
- 3
-1
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2
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66
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Unsupervised Clustering
My research is about comparing K-means and DBSCAN, and Im using unsupervised learning method in clustering.
Is it true that the number of cluster in K-means is also the same number as the unique ...
1
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2
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12k
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In k-means clustering, why sum of squared errors (SSE) always decrease per iteration?
In k-means clustering, why sum of squared errors (SSE) always decrease per iteration?
How can prove it by mathematical derivation of formulas?
k : number of clusters
m : number of examples
$c_h$ : ...
- 351
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2
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486
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How to include percentage variables in PCA + K-means when some values are undefined because the denominator is 0?
I'm trying to do customer segmentation by using PCA to reduce dimensionality and then feeding the resulting principal components into a K-means algo to get at the final segments. Some of my variables ...
- 1,554
0
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0
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1k
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Training a neural net as a classifier when there are no labels
I have an ML problem where I have a large data set and in this data set there are N categories. We have no labels. I want to be able to take this data set and use it to train a neural network to ...
- 563
1
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1
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2k
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Difference between Hartigan & Wong Algo to Lloyd's algorithm in K-means clustering
In the iterations of Hartigan and Wong Algo of K-Means clustering, If the centroid is updated in the last step, for each data point included, the within- cluster sum of squares for each data point if ...
- 1,195
3
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2
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2k
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Kmeans - Does removing outliers on a dimension affect other dimensions clustering prediction?
I have a set with several features that I wish to cluster using Kmeans. If I remove a point that is an outlier on one dimenssion but not in the others will it affect the result?
Outliers were found ...
- 193
0
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1
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79
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Determine the most important documents for supervised learning
I have somewhat of a general/high level question.
Assume I'm doing supervised machine learning on some text data (tweets for example) and categorizing the documents to a certain taxonomy (multi-class ...
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1
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2
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48
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is finding danger zones in a map considered as clustering problem?
if I have a data-set of places where accidents happened in certain city , is identifing danger zones in that city considered as clustering problem ?
if for example I use KMeans , I would have to pass ...
2
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0
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114
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Clustering data sitting close to corners of an N-dimensional parallelepiped
I am looking for a method of clustering data that are close to the corners of an $N$-dimensional parallelepiped (but I don't know the vectors spanning it). Is there a good method for finding ...
- 21
3
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5
answers
2k
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An algorithm similar to (or based on) K-means that do not require the 'k' number of clusters
These days I'm using a lot (and discovering) nice ways to use k-means' clustering. For example, clustering word embeddings (word2vec vectors) to find synonyms or clustering doc vectors (doc2vec) to ...
- 163
0
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3
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2k
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Prediction after PCA and K-Means
I have a data set with a large amount of features.
I'm applying PCA on it in order to run it through K-means, to discover clusters in my data set.
I'd like to know what is the best practice to make ...
- 1