2015/12/24 by Zhi‐Hong Deng, Deng, Zhi-Hong
Computer Science · #Data Management and Algorithms #Data Mining Algorithms and Applications #Data Structures and Algorithms (cs.DS) #Databases (cs.DB) #FOS: Computer and information sciences #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.1512.07806
openalex publication_date 2015/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.