Clustering customer's current profile using hierarchical clustering / Mohd Hairi Mohd Zin

Clustering is a data mining activity that aims to differentiate groups inside a given set of objects, with respect to a set of relevant attributes of the analyzed objects. Generally, existing clustering methods start with a known set of objects, measured against a known set of attributes. But there are numerous applications where the attribute set characterizing the objects evolves. This paper proposed an incremental clustering method based on a hierarchical clustering, that is capable to re-partition the object set, when the attribute set increases. The method starts from the partitioning into clusters that was established by applying the Hierarchical clustering (HC) before the attribute set changed. The current load profile can also indicate the type of consumers that connected to the feeder. In order to compare the performance of hierarchical clustering, a cophenetic correlation coefficient was used. The closer the value of the cophenetic correlation coefficient is to one, the more accurately the clustering solution reflects the data.