CLASTERING TYPE OF BEST GRAMEDIA PUBLISHER SELLER PAPER USING SHRINKING SHARED NEAREST NEIGHBORS METHOD
Over time in the business world of book publishing has increased the number of publishers is very significant. This has triggered a very tight competition to grab the attention of customer for the next operational activity. The publisher must be able to guess which type of book the customer candidate will be interested in. Given these problems, the publishing industry is still having trouble determining what kind of books will attract potential readers. So this situation hampers publishers to be able to create the kind of book that the readers want. Then to make it easier to determine and know the best seller type book group is the author uses one way that is by using SSNN method. SNN-based data shrinking algorithm (SSNN) uses the concept of data movement from the shrinking data algorithm to increase the accuracy obtained. The concept of data movement will strengthen the density of adjacent graph so that cluster forming process can be done from neighboring graph components that still have adjacency relationship. In this trial using data order gramedia property and using 1200 test data which is divided into 4 groups of test data. Produces clusters that generate the amount of data per genre on each cluster. And generate the children's book genre as a genre of best seller books.