Questions tagged [spectral-clustering]

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What's the fastest clustering package in Python?

I'd like to perform clustering analysis on a dataset with 1,300 columns and 500,000 rows. I've seen that clustering algorithms are available in SciKit-Learn. But I'm worried that the algorithms will ...
Connor's user avatar
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MEL VS linear spectrograms for bioacoustics machine learning

I don't have background in bioacoustics but working on a data-science project in bioacoustics. I am working with animal vocalizations recorded at sampling rate of 250000. Animals are bats, which are ...
user305883's user avatar
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best algorithms for clustering customers, customer segmentation

I have a dataset mixture of categorical and numerical variable, I was wonder what are the best algorithms to cluster customers? how to find the underlying patterns that segments a customer??
F.Hazratian's user avatar
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understanding quadratic form in proof of positive definiteness of laplacian matrix

Consider the proof at page 2 found here: I cant wrap my head around the 2nd and third line: \begin{align} &= \sum_{i \in V}x(i)^2 ...
user1001020202's user avatar
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Derive quadratic form of this laplacian matrix

Lets look at the following laplacian: $L = I - \frac{1}{d}A$ and the graph $H = (U, V)$. I am trying to derive the known quadratic form of this laplacian $x^T Lx = \frac{1}{d}\sum_{u, v \in H}(x_u - ...
user100101's user avatar
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What clustering algorithm is best for dataset with only binary categorical features

I have a dataset with a lot of binary categorical features and a single continuous target value. I would like to cluster them but I am not quite sure what to use? I have in the past used dbscan for ...
Oliver's user avatar
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1 vote
1 answer

What are the benefits of using spectral k-means over simple k-means?

I have understood why k-means can get stuck in local minima. Now, I am curious to know how the spectral k-means helps to avoid this local minima problem. According to this paper A tutorial on ...
Amartya's user avatar
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Why kmeans cluster breakup is like this [closed]

I have a galaxy 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 ...
Ayan Mitra's user avatar
2 votes
1 answer

Ways of calculating the area of colored regions in a map

Background I am a PHD student trying to improve my data science. One of my research projects, has me tasked with determining the size of the clusters in a colored image of regions. Here is an example ...
Gabriel Fair's user avatar
1 vote
1 answer

Clusterize Spectrum

I have pandas table which contains data about different observations, each one was measured in different wavlength. These observsations are different than each other in the treatment they have gotten. ...
Reut's user avatar
  • 349
1 vote
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Which version of spectral clustering in sklearn library?

Which version of spectral clustering is implemented in sklearn library? Is it Shi, Malik or Ng, Jordan, Weiss from this tutorial? In sklearn user guide, both versions are mentioned in reference. From ...
Munira's user avatar
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2 votes
1 answer

How to use spectral clustering to predict?

In an academic paper, they talk about using a nearest neighbour algorithm to predict the cluster of a new point. And how the number of nearest neighbours is set to 10 in their example. What do they ...
strateeg32's user avatar
12 votes
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What are practical differences between kernel k-means and spectral clustering?

I've been lately wondering about kernel k-means and spectral clustering algorithms and their differences. I know that spectral clustering is a more broad term and different settings can affect the ...
Kuba_'s user avatar
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