Looking for an algorithm which does Max Sum Clustering Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsClustering not producing even clustersHow to create clusters of position data?Numerical data and different algorithmsHow PCA is different from SubSpace clustering ? how to extract variables responsible for PCA1 component?Distance Based Classification in PythonWhy use SOM for clustering?Datasets for Weighted graph clustering with detailed ground truthData model and algorithm for recommending “related” interestsMeasure of variety within list/clusterClustering algorithm for a distance matrix
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Looking for an algorithm which does Max Sum Clustering
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsClustering not producing even clustersHow to create clusters of position data?Numerical data and different algorithmsHow PCA is different from SubSpace clustering ? how to extract variables responsible for PCA1 component?Distance Based Classification in PythonWhy use SOM for clustering?Datasets for Weighted graph clustering with detailed ground truthData model and algorithm for recommending “related” interestsMeasure of variety within list/clusterClustering algorithm for a distance matrix
$begingroup$
I have a very limited background in data science and dataset processing and I was hoping I could get some help here. I am doing some work that requires clustering certain data points having (x, y) position values and associated weight Wi for each point.
I started with looking at k-means clustering since I know how many bins or k clusters the data needs to be divided into but I also have an additional criteria regarding the sum of weights Wi in each cluster being less than/equal to a value Wmax.
I need to essentially cluster the points along k different y=Ci lines on XY plane such that the sum of weights for points along each line is less than/equal to Wmax. Are there any constrained clustering algorithms that do this. It would a massive help if someone could point me to any open source C++ data science libraries that can help me achieve this.
Thank you for your time and help.
clustering
asked 23 mins ago
confusdius730confusdius730
11
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I have a very limited background in data science and dataset processing and I was hoping I could get some help here. I am doing some work that requires clustering certain data points having (x, y) position values and associated weight Wi for each point.
I started with looking at k-means clustering since I know how many bins or k clusters the data needs to be divided into but I also have an additional criteria regarding the sum of weights Wi in each cluster being less than/equal to a value Wmax.
I need to essentially cluster the points along k different y=Ci lines on XY plane such that the sum of weights for points along each line is less than/equal to Wmax. Are there any constrained clustering algorithms that do this. It would a massive help if someone could point me to any open source C++ data science libraries that can help me achieve this.
Thank you for your time and help.
clustering
asked 23 mins ago
confusdius730confusdius730
11
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
This is really more of an optimization problem than a traditional clustering problem. Under the hood, kmeans is just moving points towards the nearest center, then recalculating the clusters after points have moved. Instead, treat this as a travelling salesman-type problem, where each Wmax is the "size of the truck" - figure out how to allocate your points effectively as if they were locations on a route. Alternatively, run a standard K-means algorithm, then figure out how to most effectively shift points to other clusters to meet your constraints.
$endgroup$
– flyingmeatball
2 mins ago
add a comment |
$begingroup$
I have a very limited background in data science and dataset processing and I was hoping I could get some help here. I am doing some work that requires clustering certain data points having (x, y) position values and associated weight Wi for each point.
I started with looking at k-means clustering since I know how many bins or k clusters the data needs to be divided into but I also have an additional criteria regarding the sum of weights Wi in each cluster being less than/equal to a value Wmax.
I need to essentially cluster the points along k different y=Ci lines on XY plane such that the sum of weights for points along each line is less than/equal to Wmax. Are there any constrained clustering algorithms that do this. It would a massive help if someone could point me to any open source C++ data science libraries that can help me achieve this.
Thank you for your time and help.
clustering
asked 23 mins ago
confusdius730confusdius730
11
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a very limited background in data science and dataset processing and I was hoping I could get some help here. I am doing some work that requires clustering certain data points having (x, y) position values and associated weight Wi for each point.
I started with looking at k-means clustering since I know how many bins or k clusters the data needs to be divided into but I also have an additional criteria regarding the sum of weights Wi in each cluster being less than/equal to a value Wmax.
I need to essentially cluster the points along k different y=Ci lines on XY plane such that the sum of weights for points along each line is less than/equal to Wmax. Are there any constrained clustering algorithms that do this. It would a massive help if someone could point me to any open source C++ data science libraries that can help me achieve this.
Thank you for your time and help.
clustering
clustering
asked 23 mins ago
confusdius730confusdius730
11
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 23 mins ago
confusdius730confusdius730
11
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 23 mins ago
confusdius730confusdius730
11
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 23 mins ago
confusdius730confusdius730
11
asked 23 mins ago
confusdius730confusdius730
11
11
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
confusdius730 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
This is really more of an optimization problem than a traditional clustering problem. Under the hood, kmeans is just moving points towards the nearest center, then recalculating the clusters after points have moved. Instead, treat this as a travelling salesman-type problem, where each Wmax is the "size of the truck" - figure out how to allocate your points effectively as if they were locations on a route. Alternatively, run a standard K-means algorithm, then figure out how to most effectively shift points to other clusters to meet your constraints.
$endgroup$
– flyingmeatball
2 mins ago
add a comment |
$begingroup$
This is really more of an optimization problem than a traditional clustering problem. Under the hood, kmeans is just moving points towards the nearest center, then recalculating the clusters after points have moved. Instead, treat this as a travelling salesman-type problem, where each Wmax is the "size of the truck" - figure out how to allocate your points effectively as if they were locations on a route. Alternatively, run a standard K-means algorithm, then figure out how to most effectively shift points to other clusters to meet your constraints.
$endgroup$
– flyingmeatball
2 mins ago
$begingroup$
This is really more of an optimization problem than a traditional clustering problem. Under the hood, kmeans is just moving points towards the nearest center, then recalculating the clusters after points have moved. Instead, treat this as a travelling salesman-type problem, where each Wmax is the "size of the truck" - figure out how to allocate your points effectively as if they were locations on a route. Alternatively, run a standard K-means algorithm, then figure out how to most effectively shift points to other clusters to meet your constraints.
$endgroup$
– flyingmeatball
2 mins ago
$begingroup$
This is really more of an optimization problem than a traditional clustering problem. Under the hood, kmeans is just moving points towards the nearest center, then recalculating the clusters after points have moved. Instead, treat this as a travelling salesman-type problem, where each Wmax is the "size of the truck" - figure out how to allocate your points effectively as if they were locations on a route. Alternatively, run a standard K-means algorithm, then figure out how to most effectively shift points to other clusters to meet your constraints.
$endgroup$
– flyingmeatball
2 mins ago
add a comment |
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$begingroup$
This is really more of an optimization problem than a traditional clustering problem. Under the hood, kmeans is just moving points towards the nearest center, then recalculating the clusters after points have moved. Instead, treat this as a travelling salesman-type problem, where each Wmax is the "size of the truck" - figure out how to allocate your points effectively as if they were locations on a route. Alternatively, run a standard K-means algorithm, then figure out how to most effectively shift points to other clusters to meet your constraints.
$endgroup$
– flyingmeatball
2 mins ago