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# Clustering high-dimensional data

Hi,

I try to use DBSACN to detect the outlier in my data set but it is difficult to set the parameters (epsilon,min points). Does anyone have an idea to solve the problem? it is possible to consider two clustering algorithms and each algorithm only consider sub-attributes of data set and i detect the outlier based on the results of two clustering algorithm?

Thanks

I try to use DBSACN to detect the outlier in my data set but it is difficult to set the parameters (epsilon,min points). Does anyone have an idea to solve the problem? it is possible to consider two clustering algorithms and each algorithm only consider sub-attributes of data set and i detect the outlier based on the results of two clustering algorithm?

Thanks

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## Answers

1,635UnicornIf you want to try to use two clustering algorithms based on different attributes, you'll need to multiply/split your dataset and feed one set of attributes to the first algorithm and a different set of attributes to the second algorithm, get the assigned clusters, and then join the two datasets back together again to compare.

Lindon Ventures

Data Science Consulting from Certified RapidMiner Experts

13Learner II am a beginner in rapidminer. Will the results be different, if i use two clustering algorithms based on different attributes instead of one clustering algorithm on those attributes?

Thank you

1,635UnicornLindon Ventures

Data Science Consulting from Certified RapidMiner Experts