Mining Top-K Co-Occurrence Items

TL;DR

This paper introduces a new task called top-k co-occurrence item mining, proposing efficient algorithms and data structures to identify the most frequently co-occurring items with a given set, outperforming baseline methods.

Contribution

The paper presents a novel mining task, Pi-Tree data structure, and two algorithms, PT and PT-TA, that significantly improve efficiency and scalability in top-k co-occurrence item mining.

Findings

PT algorithm outperforms baseline algorithms in execution time.

PT-TA with pruning further enhances efficiency.

Algorithms demonstrate excellent scalability on synthetic and real data.

Abstract

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.