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Although Eclat algorithm is an efficient algorithm for mining association rules, there are some disadvantages which limit the efficient of Eclat. In this paper, we proposed an improved Eclat algorithm called Eclat_growth which is based on the increased search strategy. There are three main steps in the Eclat_growth algorithm. First, it scans the database and stores it into a table using vertical data format. Then, it builds an increased two-dimensional pattern tree and the TID_sets of itemsets in the vertical data format table are added into the pattern tree row by row. New frequent itemsets are generated by combining the new added item data with the existing frequent itemsets in the pattern tree. Finally, all frequent itemsets can be found by picking up all nodes of the pattern tree. In the process of generating new frequent itemsets, the prior knowledge is used to fully clip the candidate itemsets. In the process of generating an intersection of two itemsets and calculating the support degree, we proposed a new method called BSRI (Boolean array setting and retrieval by indexes of transactions) to reduce the run time. By comparing Eclat_growth with Eclat, Eclat-diffsets, Eclat-opt and hEclat, it is indicated that Eclat_growth has the highest performance in mining associating rules from various databases.