  Frequent itemset mining has emerged as a fundamental problem in data mining
and plays an important role in many data mining tasks, such as association
analysis, classification, etc. In the framework of frequent itemset mining, the
results are itemsets that are frequent in the whole database. However, in some
applications, such recommendation systems and social networks, people are more
interested in finding out the items that occur with some user-specified
itemsets (query itemsets) most frequently in a database. In this paper, we
address the problem by proposing a new mining task named top-k co-occurrence
item mining, where k is the desired number of items to be found. Four baseline
algorithms are presented first. Then, we introduce a special data structure
named Pi-Tree (Prefix itemset Tree) to maintain the information of itemsets.
Based on Pi-Tree, we propose two algorithms, namely PT (Pi-Tree-based
algorithm) and PT-TA (Pi-Tree-based algorithm with TA pruning), for mining
top-k co-occurrence items by incorporating several novel strategies for pruning
the search space to achieve high efficiency. The performance of PT and PT-TA
was evaluated against the four proposed baseline algorithms on both synthetic
and real databases. Extensive experiments show that PT not only outperforms
other algorithms substantially in terms execution time but also has excellent
scalability.
