All Questions
Tagged with k-means k-nearest-neighbour
20 questions
2
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1
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k-means/k-nearest neighbours on multi-dimensional scaled data
I used the Python manifold library for multi-dimensional scaling on my distance matrix. Can I use k-means or k-nearest neighbours on ...
CommunityBot
- 1
2
votes
1
answer
504
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Can the k-nearest neighbor algorithm tell you how many clusters there are among predictors?
I recently did a short course on machine learning in R and found the k-means and k-nearest neighbor techniques extremely interesting.
Forgive my naivete if this is all wrong, but it seems like the ...
- 58.2k
0
votes
0
answers
254
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Classification on aggregate data
I've been tasked with creating a model that can classify individual cases as either something to be flagged or not. However, the training data I have access to is only aggregate data, where each row ...
0
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1
answer
125
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KNN and K-means, very different but possible equivalency?
Why does the k-nearest neighbor algorithm and k-means clustering algorithm with $k=1$ act the same?
- 962
1
vote
2
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196
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Is it possible to have same result for knn classifier and kmeans?
Could we achieve similar grouping or results for a set of data, if applied with either Knn and k-means
- 46.8k
0
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0
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232
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Which is the best clustering algorithm for clustering multidimensional data with low density difference?
I am working on a project currently and I wish to cluster multi-dimensional data. I tried K-Means clustering and DBSCAN clustering, both being completely different algorithms.
The K-Means model ...
1
vote
0
answers
162
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python find the optimal # of cluster for K-Means algorithm
I have a data that contains 24 features and all features have some missing values. I want to use the impute-KNN algorithm from sklearn to fill the missing values. However, before I do that, I think I ...
- 21
2
votes
1
answer
801
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What is the difference in application between KNN and K-means
I know that KNN is a supervised learning method and K-means is an unsupervised clustering method. I also know their algorithms.
What I am confused about is that what is the point having K-means given ...
- 299
0
votes
1
answer
3k
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In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbours. Why not manhattan distance?
In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbours. Why not manhattan distance ?
- 58.2k
26
votes
1
answer
18k
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Does Dimensionality curse effect some models more than others?
The places I have been reading about dimensionality curse explain it in conjunction to kNN primarily, and linear models in general. I regularly see top rankers in Kaggle using thousands of features on ...
- 58.8k
2
votes
2
answers
2k
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Why are k-means and k-NN considered simple algorithms in machine learning?
We all know the k-means clustering algorithm and the k-nearest neighbors algorithm: the former is an unsupervised clustering method, and the latter is a supervised learning technique in machine ...
CommunityBot
- 1
3
votes
0
answers
956
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Difference between identifying outliers using LOF and K-means clustering
I am identifying outliers using K-means and LOF (Local Outlier Factor). Let's say if we are identifying possible outliers using both the techniques, I believe LOF will pick global outliers also as ...
CommunityBot
- 1
135
votes
5
answers
175k
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What are the main differences between K-means and K-nearest neighbours?
I know that k-means is unsupervised and is used for clustering etc and that k-NN is supervised. But I wanted to know concrete differences between the two?
- 289
4
votes
0
answers
4k
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Distance metric for categorical and numerical data
I have asked a related question in mathematics section, but I think here is a better place to ask.
for both KNN algorithm (classification) and k-means algorithm (clustering), there is a need for a ...
- 58.8k
-1
votes
1
answer
2k
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Clustering a list of similar categorical words and phrases in python
I am trying to cluster a list of words/phrases in the context of similarity (not semantic).
I have a large dataset of categorical variables. In a perfect world, the categorical variables would have a ...
- 42.9k
1
vote
1
answer
46
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K mean clustering
I have coordinates data set (X.Y) with an additional attribute "Z". I want to cluster the data into 5 clusters based on X and Y but I want to add some constrains on how much the sum of "Z" can be at ...
- 42.9k
1
vote
0
answers
3k
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Cosine similarity and normalization
When I normalize a data set and compute the cosine similarity between the rows, the cosine similarity differs from the one without any normalization.
Say there are 4 2D vectors: (1, 1), (2, 2), (1, 2)...
- 131
3
votes
2
answers
2k
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How to predict property value using lat/lon?
I have lat/lon and property values for households in a particular region.
Format:
Lat Lon value
32.2 -98.22 120000
....
Now I have new data of the ...
CommunityBot
- 1
0
votes
1
answer
56
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Classifying a set of photos to places
I want to cluster photos and map them to places.
As input I have
Photos with locations (lat, long)
Places - some as (imprecise) bounding boxes, some just as points, maybe others as bounding ...
- 42.9k
5
votes
2
answers
4k
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Does K-means incorporate the K-nearest neighbour algorithm?
I was watching this tutorial on K-means clustering and from what I understand K-means is:
Randomly generate the centroids for k clusters
Create a classification model dividing into k regions (Do we ...
- 3,112