All Questions
54 questions
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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
votes
1
answer
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
vote
1
answer
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
answer
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
answer
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
0
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0
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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
0
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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 ...
- 1
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1
answer
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 ...
- 3
4
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1
answer
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
3
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1
answer
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
answers
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
votes
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?
- 51
1
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1
answer
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/...
- 51
1
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1
answer
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
votes
2
answers
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
4
votes
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
views
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
answer
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 ...
- 11
1
vote
1
answer
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
vote
2
answers
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
0
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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
2
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2
answers
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
1
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1
answer
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
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
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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
3
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1
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1k
views
Which methods can help us to understand clustering model is good or bad?
In some clustering algorithm, ex: K-Means cluster, it is very sensitive with outliers, so we need to remove outliers before aplly ...
- 160
0
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0
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70
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How can I use the results of clustering algorithms for classification
I'm doing a mobile customer segmentation and I was using K-means to cluster my data according to the various data points (location, time of use, duration used for etc). After reading a lot of posts in ...
- 101
1
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1
answer
1k
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Threshold for kmeans anomaly detection
I'm learning the kmeans to find out anomaly from the dataset.
but I don't know how to set threshold.
I tried by the putting mean of the centroid to point distance but it's not working, half my record ...
- 141
1
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1
answer
318
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What is the typical taxonomy for clustering methods?
What is the typical taxonomy for clustering methods?
For example, for regression we can talk about: simple regression, multiple univariate regression, and multivariate regression. And then, we can ...
user162200
5
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2
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23k
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How to assign new data to an existing clustering
I have the following case.
Say I have a set of 100 celebrities and I form 4 clusters using k-means. Lets assume that these 4 clusters are music, sports, politics, movies.
Now say if I want to ...
- 153
1
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0
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448
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Cluster validation method for no cluster labels and differently sized clusters
I'm primarily a programmer and have little to no training in formal maths or statistics of any kind.
I'm working on my dissertation (which foolishly is about clustering data), the process is ...
- 193
3
votes
2
answers
1k
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Using clustering for unsupervised classification (visualizing k-means cluster centers)
I know that the cluster centroid is the middle of a cluster. It's a vector containing one number for each variable, where each number is the mean of a variable for the observations in that cluster.
I ...
- 43
21
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4
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25k
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How to understand the drawbacks of Hierarchical Clustering?
Can someone explain the pros and cons of Hierarchical Clustering?
Does Hierarchical Clustering have the same drawbacks as K means?
What are the advantages of Hierarchical Clustering over K means?
...
- 5,683
4
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1
answer
1k
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Incorporating new words in tfidf feature-vector for online clustering
I am building an Online news clustering system using Lucene and Mahout libraries in java. I intend to use vector space model and tfidf weights for Kmeans(or fuzzy/streamKmeans).
My plan is : Cluster ...
- 41
5
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1
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3k
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K-means and maximum likelihood!
Is there any relation between k-means and the maximum-likelihood estimate in unsupervised learning? Any references would be appreciates!
- 245
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1
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609
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How to measure the similarity of k-means clustering using different datasets?
I run k-means clustering on my dataset (100 samples in total) and partition the data into k=5 clusters. Then I want to test how robust of the k-means can be; however, I haven't got more new data ...
- 1,759
1
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2
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270
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A clustering and classification question
I'm trying to classify my set of data into two classes (introvert / extrovert). I was thinking of using a decision tree at first, but I don't have any potential known results in order to create my ...
- 21
8
votes
2
answers
4k
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Why only the mean value is used in (K-means) clustering method?
In clustering methods such as K-means, the euclidean distance is the metric to use. As a result, we only calculate the mean values within each cluster. And then adjustments are made on the elements ...
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