The role of dimensionality reduction with regard to Clustering approaches
Hello Community,
I plan to evaluate several Clustering techniques on a TF-IDF bag of words representation where I've previously executed a feature selection to efficiently reduce the number of dimensions of my vector space. In this sense, I've read that Feature Extraction/Transformation approaches get better results with regard to dimensionality reduction in comparison to Feature Selection ones if Clustering algorithms will be applied afterwards. First of all, how do you see this opinition out of theory?
Secondly, as explained I've still executed Feature Selection. Would it be correct to additionally execute Feature Extraction based on the remaining dimensions which were derived from Feature Selection? Or should the Feature Exraction for efficient Clustering should be applied on the initial rough dataset?
I thank you all for the participation and for the answers!
Best regards!
I plan to evaluate several Clustering techniques on a TF-IDF bag of words representation where I've previously executed a feature selection to efficiently reduce the number of dimensions of my vector space. In this sense, I've read that Feature Extraction/Transformation approaches get better results with regard to dimensionality reduction in comparison to Feature Selection ones if Clustering algorithms will be applied afterwards. First of all, how do you see this opinition out of theory?
Secondly, as explained I've still executed Feature Selection. Would it be correct to additionally execute Feature Extraction based on the remaining dimensions which were derived from Feature Selection? Or should the Feature Exraction for efficient Clustering should be applied on the initial rough dataset?
I thank you all for the participation and for the answers!
Best regards!