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Picking a sklearn clustering algorithm based on Choosing a distance metric and measuring similarity vector

I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I define similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation forHere's what algorithm might be most appropriate for this or do I need to write my owndata looks like:

data

I'm wondering what similarity measure would make sense? I work in python, so prefer a pythonic/sci-kit learn way of doing this.

Picking a sklearn clustering algorithm based on a similarity vector

I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I define similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation for what algorithm might be most appropriate for this or do I need to write my own? I work in python, so prefer a pythonic way of doing this.

Choosing a distance metric and measuring similarity

I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I define similarity as weighted comparison of features. I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Here's what my data looks like:

data

I'm wondering what similarity measure would make sense? I work in python, so prefer a pythonic/sci-kit learn way of doing this.

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I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I defineddefine similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation for what algorithm might be most appropriate for this or do I need to write my own? I work in python, so prefer a pythonic way of doing this.

I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I defined similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation for what algorithm might be most appropriate for this or do I need to write my own? I work in python, so prefer a pythonic way of doing this.

I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I define similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation for what algorithm might be most appropriate for this or do I need to write my own? I work in python, so prefer a pythonic way of doing this.

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kms
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I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I defined similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation for what algorithm might be most appropriate for this or do I need to write my own? I work in python, so prefer a pythonic way of doing this.

I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I defined similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation for what algorithm might be most appropriate for this or do I need to write my own? I work in python, so prefer pythonic way of doing this.

I am trying to decide which particular algorithm would be most appropriate for my use-case.

I have dataset of about 1000 physical buildings in a city with feature space such as location, distance, year built and other characteristics etc. For each new data point, a building, I'd like to find 3-5 buildings that are most similar based on feature space comparison.

I defined similarity as weighted comparison of features. sklearn clustering algorithms create clusters by trying to separate data points into different groups which isn't quite what I am looking for.

I'd like to iterate over entire feature space (w/ filter like location) and choose 3-5 most similar buildings matching the new building data point.

Any recommendation for what algorithm might be most appropriate for this or do I need to write my own? I work in python, so prefer a pythonic way of doing this.

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