روشی کارا برای کاوش مجموعه اقلام پرتکرار در تحلیل داده‌های سبد خرید

Discovery of hidden and valuable knowledge from large data warehouses is an important research area and has attracted the attention of many researchers in recent years. Most of Association Rule Mining (ARM) algorithms start by searching for frequent itemsets by scanning the whole database repeatedly and enumerating the occurrences of each candidate itemset. In data mining problems, the size of data is often too large to fit in main memory. However, in some cases such as records of sales of a large supermarket, the probability of a particular item to be present in a transaction is often very low. This is due to the fact that a large number of items are usually available for purchase and also the fact that a small set of items is purchased by a customer in a shopping. In this paper, we make use of these facts to propose an efficient method for mining frequent itemsets. In our approach, the database is scanned just once, and data is encoded into a compressed form and held in a proper data structure in main memory. In each iteration, the time required to measure the frequency of itemsets, is reduced further (i.e., enumerating n-dimensional candidate itemsets is much faster than (n-1)-dimensional itemsets). We evaluate the efficiency of our technique using both synthetic and real-life datasets and compare it with other ARM methods proposed in past research