The Research And Application Of Clustering Analysis In Customer Segmentation

This paper analyses the clustering technology in data mining and its current research status in customer segmentation. Additionally, the K-means algorithm based on partition and the DBSCAN algorithm based on density have been studied and analyzed thoroughly. Combining advantages with disadvantages of the two algorithms, the improved algorithm DBSK is proposed. Because of the partition of data set, DBSK reduces the requirement of memory; the method of computing variable value is put forward; to the uneven data set, because of adopting different variable values in each local data set, the dependence on global parameters is reduced, so the clustering result is better. Emulation experiment has been carried out, which proves the algorithm's feasibility and validity. At last, the paper introduces correlative concepts of customer segmentation, expatiates the design and realization of customer segmentation system based on clustering technology.