Data warehouse is designed for answering analytical queries. Data warehouse saves historical data. In the data warehouse, the response time to analytical queries is long. So reducing the response time is a critical problem. There are a lot of algorithms to solve the problem. Some of them, materialize frequent views. The previously posed queries have important information that will be used in the future. This paper proposes an algorithm for view materialization. The proposed algorithm finds proper views using previous queries and materializes them. The views are able to answer future queries. The view selection algorithm has four steps. At first, it clusters previous queries by SOM method. Then frequent queries are found by Apriori algorithm. In the third step the problem is converted to 0/1 knapsack equations and finally, optimal queries are joined to create only one view for each cluster. This paper improves the first and third step. This paper uses the SOM algorithm for clustering previous queries in the first step and it solves the 0/1 knapsack equations according to shuffled frog leaping algorithm in the third step. Experimental results show that it improves the previous view selection algorithms according to response time and storage space factor.