besides thiswe have several methods that recently came into the clustering landscapelike birch and clarans dimensionality reduction once we start extracting attributes or features from raw data samplessometimes our feature space gets bloated up with humongous number of features this poses multiple challenges including analyzing and visualizing data with thousands or millions of featureswhich makes the feature space extremely complex posing problems with regard to training modelsmemoryand space constraints in fact this is referred to as the "curse of dimensionalityunsupervised methods can also be used in these scenarioswhere we reduce the number of features or attributes for each data sample these methods reduce the number of feature variables by extracting or selecting set of principal or representative features there are multiple popular algorithms available for dimensionality reduction like principal component analysis (pca)nearest neighborsand discriminant analysis figure - shows the output of typical feature reduction process applied to swiss roll structure having three dimensions to obtain two-dimensional feature space for each data sample using pca figure - unsupervised learningdimensionality reduction from figure - it is quite clear that each data sample originally had three features or dimensionsnamely ( and after applying pcawe reduce each data sample from our dataset into two dimensionsnamely '( dimensionality reduction techniques can be classified in two major approaches as follows feature selection methodsspecific features are selected for each data sample from the original list of features and other features are discarded no new features are generated in this process feature extraction methodswe engineer or extract new features from the original list of features in the data thus the reduced subset of features will contain newly generated features that were not part of the original feature set pca falls under this category