Abstract: Clustering analysis is a very useful tool in the domain of data mining for searching distributing mode from a great deal of data. Its main algorithms are partition-based algorithm, hierarchy-based algorithm, density-based algorithm, grid-based algorithm, and model-based algorithm. The paper mainly discusses a clustering algorithm based on density and grid in data mining, which has high clustering efficiency and low time complexity. It is efficient and effective for multi-density and uniformity density data sets with noise and suitable for batch update. After that an incremental clustering technique is presented. This technique not only makes best use of the former clustering results and improves the efficiency of clustering analysis, but also brings to the reduction of enormous expenditure on knowledge base maintenance. At last an application of the algorithm in Customer Relationship Management (CRM) is gien.