Clustering is an important task in mining the evolving data streams. A lot of data streams are high dimensional in nature. Clustering in the high dimensional data space is a complex problem, which is inherently more complex for data streams. Most data stream clustering methods are not capable of dealing with high dimensional data streams; therefore they sacrifice the accuracy of clusters. In order to solve this problem we proposed an adaptive grid -based clustering method. Our focus is on providing up-to-date arbitrary shaped clusters along with improving the processing time and bounding the amount of the memory u sage. In our method (B+C tree), a structure called “B+cell tree” is used to keep the recent information of a data stream. In order to reduce the complexity of the clustering, a structure called “cluster tree” is proposed to maintain multi dimensional clusters. A Cluster tree yields high quality clusters by keeping the boundaries of clusters in a semi -optimal way. Cluster tree captures the dynamic changes of data streams and adjusts the clusters. Our performance study over a number of real and synthetic data streams demonstrates the scalability of algorithm on the number of dimensions and data without sacrificing the accuracy of identified clusters

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