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Association rules mining from transaction data can be used to recommend the items that are often purchased together frequently. However, it is difficult to set minimum support threshold. If the minimum support threshold is set too high, then there may be only a small or even no result. If the threshold is set too low, it may generate many uninteresting associations. In addition, each supporting a different set of data, enabling users to find the optimal difficult. This paper presents a new approach to the collection frequency by using top weight of complete symmetric digraphs. Using the top weight, the association rule with the maximum support can be calculated and it works with any dataset.
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