Fast Core-based Top-k Frequent Pattern Discovery in Knowledge Graphs

Zeng, Jian; U, Leong Hou; Xiao, Yan; Han, Mingji; Tang, Bo

doi:10.1109/icde51399.2021.00086

Cited by 8 publications

(9 citation statements)

References 37 publications

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“…Wang et al, 2021) proposed a metric to measure the quality of a pattern and developed a parallel algorithm with early termination property to efficiently discover k best patterns in a distributed large graph. FastPat framework (Zeng, U, Yan, Han, & Tang, 2021) utilizes the meta index and an upper bound of the frequency score to prune unqualified candidates. In particular, FastPat efficiently calculates the support of candidates through a join-based approach.…”

Section: Related Workmentioning

confidence: 99%

Near-optimal Top-k Pattern Mining

Wang,

Lan,

et al. 2022

Preprint

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Nowadays, frequent pattern mining (FPM) on large graphs receives increasing attention, since it is crucial to a variety of applications, e.g., social analysis. Informally, the FPM problem is defined as finding all the patterns in a large graph with frequency above a userdefined threshold. However, this problem is nontrivial due to the unaffordable computational and space costs in the mining process. In light of this, we propose a cost-effective approach to mining near-optimal top-k patterns. Our approach applies a "level-wise" strategy to incrementally detect frequent patterns, hence is able to terminate as soon as top-k patterns are discovered. Moreover, we develop a technique to compute the lower bound of support with smart traverse strategy and compact data structures. Extensive experimental studies on real-life and synthetic graphs show that our approach performs well, i.e., it outperforms traditional counterparts in efficiency, memory footprint, recall and scalability.

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Section: Related Workmentioning

confidence: 99%

Near-optimal Top-k Pattern Mining

Wang,

Lan,

et al. 2022

Preprint

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“…On the other hand, top-K FIM is considered a computationally harder task to perform as compared to support-based FIM. Due to the self-explanatory results, it is applied in different real world application domains such as monitoring users’ activity from their movements data [ 33 ], COVID-19 virus strains classification and identification [ 34 ], and extraction of metadata from dynamic scenarios [ 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts

Rehman

Alnazzawi

Ashraf

et al. 2022

Sensors

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Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

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“…The goal of frequent subgraph mining is to find subgraphs whose appearances are top-k frequent [4], [5] or exceed a user-defined threshold [2], [6]. We argue that frequent subgraph mining finds its real applications in tasks such as activity planning or POI recommending.…”

Section: Introductionmentioning

confidence: 99%

“…The literature evaluates the frequency of a subgraph S in a graph G by looking for isomorphisms of S in G [2], [5], [6]. Isomorphisms mean exact matches of node labels and edges in the subgraph with a pattern.…”

Section: Introductionmentioning

confidence: 99%

“…Isomorphisms mean exact matches of node labels and edges in the subgraph with a pattern. The problem is well studied in a general graph, which can be a collaboration graph without geo-attributes [2], bioinformatics network [4] or knowledge graph [5]. In this paper, different from previous work, we address a new kind of geo-social frequent pattern mining in networks with constraints on a spatial and fixed label space, hereafter called GsFPM for brevity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mining Top-k Frequent Patterns in Large Geosocial Networks: A Mnie-Based Extension Approach

Zhou

Jiang

et al. 2023

IEEE Access

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Frequent pattern mining (FPM) has played an important role in many graph domains, such as bioinformatics and social networks. In this paper, we focus on geo-social graphs, a kind of social network augmented by geographical information. However, in addition to the exponential time complexity of the problem, we face the challenge of efficient subgraph retrieval since we are interested in patterns in a specific region in such a network. For this reason, we formulate the top-k FPM problem in large geo-social networks. Specifically, we devise a novel framework for subgraph retrieval and FPM mining with a series of optimizations. First, we propose a neighboring-aware R-tree (NaR-Tree) index structure to alleviate the challenge of retrieving subgraphs from a large graph. NaR-Tree is a variant of R-tree in which each nonleaf tree node further maintains some edge statistics information for the rectangle related to it. Second, we define the concept of minimum image-based support of edges (MNIE). With the help of the NaR-Treeand MNIE-based pattern extension approach, a mining algorithm that addresses the problem of exponential candidate patterns is proposed. We also present a lazy retrieval strategy to reduce the frequency of subgraph retrieval. Finally, we adopt an edge sampling approach to further accelerate the mining process. Extensive experiments on real-world and synthesized datasets are conducted to demonstrate the effectiveness and efficiency of our solution.INDEX TERMS Frequent pattern mining, geo-social network, NaR-Tree, edge sampling.

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Fast Core-based Top-k Frequent Pattern Discovery in Knowledge Graphs

Cited by 8 publications

References 37 publications

Near-optimal Top-k Pattern Mining

Near-optimal Top-k Pattern Mining

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts

Mining Top-k Frequent Patterns in Large Geosocial Networks: A Mnie-Based Extension Approach

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