An Extenics-Based Criteria Clustering Method

Clustering has been applied in many field of management for better decision making with a lot of algorithms such as K-means. Based on Extenics, we found that most of algorithms calculate the similarity of elements in a certain set by distance to each other, they focus on the position of each element and neglect their criteria. However, in the real world, there are usually exist criteria to score the elements. Therefore, we present a new clustering method. In our method, we use distance in Extenics for similarity calculating based on criteria, and compared a simple case with traditional K-means algorithm. The results show that our method is more practical and has much potential value for data mining and knowledge management.