This document discusses patent data clustering algorithms. It begins with an introduction to patent data and clustering. It then describes limitations of traditional clustering algorithms like K-means and discusses the need for new algorithms. The document proposes a new dynamic clustering algorithm using K-medoids that considers both interconnectivity and closeness between clusters to group patent data. It concludes that this methodology can cluster various data types as long as similarity is defined and future work involves implementing the algorithm for patent mining.