' going to set the name of my application to qbslfbot and create qbsl$poufyu object that can then use to create rdds that run on my local machine we'll skip past the dsfbuf$mvtufsfe%bub function for nowand go to the first line of code that gets run ebub tdqbsbmmfmj[ tdbmf dsfbuf$mvtufsfe%bub  the first thing we're going to do is create an rdd by parallelizing in some fake data that ' creatingand that' what the dsfbuf$mvtufsfe%bub function does basicallyi' telling you to create data points clustered around centroidsand this is pretty much identical to the code that we looked at when we played with -means clustering earlier in the book if you want refreshergo ahead and look back at that basicallywhat we're going to do is create bunch of random centroids around which we normally distribute some age and income data sowhat we're doing is trying to cluster people based on their age and incomeand we are fabricating some data points to do that that returns ovnqz array of our fake data once that result comes back from dsfbuf$mvtufsfe%bubi' calling tdbmf on itand that will ensure that my ages and incomes are on comparable scales nowremember the section we studied saying you have to remember about data normalizationthis is one of those examples where it is importantso we are normalizing that data with tdbmf so that we get good results from -means and finallywe parallelize the resulting list of arrays into an rdd using qbsbmmfmj[ now our data rdd contains all of our fake data all we have to doand this is even easier than decision treeis call fbotusbjo on our training data dmvtufst fbotusbjo ebub,nby*ufsbujpot jojujbmj[bujpo pef sboepnwe pass in the number of clusters we wantour valuea parameter that puts an upper boundary on how much processing it' going to dowe then tell it to use the default initialization mode of -means where we just randomly pick our initial centroids for our clusters before we start iterating on themand back comes the model that we can use we're going to call that dmvtufst alrightnow we can play with that cluster