Analysis of data is an inherent part in the world of business to identify interesting patterns underlying in the data set. The size of the data is usually huge in the modern day application. Searching the data from the huge data set with a lesser time complexity is always a subject of interest. These data are mostly stored in tables based on relational model. Data are fetched from these tables using SQL queries. Query response time is an important quality factor for this type of system. Materialized view formation is the most common way of enhancing the query execution speed across industries. Different approaches have been applied over the time to generate materialized views. However few attempts have been made to construct materialized views with the help of Association based mining algorithms and none of those existing Association based methods measure the performance of the views in terms of both Hit-Miss ratio and view size scalability. This paper proposes an algorithm which generates a materialized view by considering the frequencies of the multiple attributes at a time taken from a database with the help of Apriori algorithm. Apriori algorithm is used to generate frequent attribute sets which are further considered for materialization. Moreover by varying the support count, changing the sizes of the frequent attributes sets; proposed methodology supports scalabilisalubrityty as well as flexibility. Experimental results are given to prove the enhanced results over existing inter-attribute analysis based materialized view formation. © Springer Nature Singapore Pte Ltd. 2017.