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Run a clusteranalysis in three dimensions

Hi guys, I'm not an expert on the field of cluster Analysis. I hope one of you can help me.

I have a set of ~3000 points and each of it has four attributes, one of them is the observation attribute. I want to get borders for each of the three other attributes which allows me to reduce the overall standard deviation in every cluster.

Please have a look at the attached picture, you can find the analysis for just two attributes, the observation attribute "telephone directory entries" and the other attribute "ground size of houses". I want you to find a number of borders which allow to define intervals, which have the smallest possible standard deviation.

I'm doing this to later determine the expected average number of telephone directory entries by only knowing the ground size.

The number of intervals depends on the number of points, they should be more than ~40.

Please contact me and we can further discuss.

Skills: Mathematics, Statistical Analysis, Statistics

See more: 3d clustering matlab, cluster analysis in r, cluster analysis in r example, multidimensional k-means clustering, multidimensional k means python, multidimensional clustering python, k means clustering 3 dimensions python, cluster analysis example

About the Employer:
( 0 reviews ) Kulmbach, Germany

Project ID: #17497969

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bbrianokoyo

Hello, I can help with your cluster analysis assignment. I have a background in computer science and mathematics with 7 years of experience. I am new on Freelancer, but I would like to do it for as cheap as $85 in a More

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ProDataAnalyzer

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dinhfreedom

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aki003iitr

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nodinh325

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DataDKR

Hello. It sounds like you want 3 separate analysis. In particular, you want the analysis of your example with the ~3000 pts applied, and then two more, presumably by simply replacing the "ground size of houses" with ea More

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JMITSolution

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yjping1986

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JSantos1985

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rahicool

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IngAndreaCiufo

Your data define a 4-dimensional space. We can use a K-Means Clustering Algorithm based on this 4 features We can study if exist an ideal number of clusters, analyzing the variance of each cluster and how it changes More

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