Methods in Clustering

Cluster Analytics helps to analyze the massive amounts of data which have accrued in this technological age. It employs the idea of clustering, or grouping, objects with similar traits within the data. The benefit of clustering is that the methods do not require any prior knowledge of the data. Hence, through cluster analysis, interpreting large data sets becomes, in most cases, much easier. However one of the major challenges in cluster analytics is determining the exact number of clusters, k, within the data. For methods such as k-means and nonnegative matrix factorization, choosing the appropriate k is important. Other methods such as Reverse Simon-Ando are not as dependent on beginning with the correct k. In this paper, we discuss these methods and apply them to several well-known data sets. We then explore techniques of deriving the number of clusters from the data set and lastly several points of theoretical interest.