As the tractable amount of data is growing in computer science area, fast clustering algorithm is being required because traditional clustering algorithms are not so feasible for very large and high dimensional data. Many studies have been reported for clustering of large database, but most of them circumvent this problem by using the approximation method to result in the deterioration of accuracy. In this paper, we propose a new clustering algorithm by means of partial maximum array, which can realize the agglomerative hierarchical clustering with the same accuracy to the brute-force algorithm and has O(N 2 ) time complexity. And we also present the incremental method of similarity computation which substitutes the scalar calculation for the time-consuming calculation of vector similarity. The experimental results show that clustering becomes significantly fast for large and high dimensional data.