Descriptive Data Mining: Segmentation

This is problem $14$ at the end of Chapter $4.$

Attracted by the possible returns from a portfolio of movies, hedge funds have invested in the movie industry by financially backing individual films and$/$or studios. The hedge fund Gelt Star is currently conducting some research involving movies involving Adam Sandler, an American actor, screenwriter, and film producer. As a first step, Gelt Star would like to cluster Adam Sandler movies based on their gross box office returns and movie critic ratings. Using the data in the file Sandler, apply k$-$means clustering with to characterize three different types of Adam Sandler movies. Based the clusters on the variables Rating and Box Office. Rating corresponds to movie ratings provided by critics $($a higher score represents a movie receiving better reviews$).$Box Office represents the gross box office earnings in $2015$ dollars. Be sure to Normalize Input Data and specify $50$ iterations and $10$ random starts in Step $2$ of the XLMinerk$-$Means Clustering procedure. Use the resulting clusters to characterize Adam Sandler movies.