TKE: Mining Top-K Frequent Episodes

Fournier‐Viger, Philippe; Yang, Yong; Yang, Peng; Lin, Jerry Chun‐Wei; Yun, Unil

doi:10.1007/978-3-030-55789-8_71

Cited by 24 publications

(8 citation statements)

References 27 publications

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“…Fifth, the type of patterns considered in this paper (itemsets) is simple and not suitable for all applications. For instance, some more complex pattern types such as episodes [6], [7] or sequential patterns could be considered [30] to handle datasets with temporal or sequential information. Besides, variations of the itemset mining problem could be studied to include additional information such as weights or contextual information.…”

Section: Discussionmentioning

confidence: 99%

Mining Productive Itemsets in Dynamic Databases

Fournier-Viger

et al. 2020

IEEE Access

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Discovering frequent itemsets is a data analysis task used in numerous domains. It consists of finding sets of items (itemsets) that frequently appear in a set of database records (also called transactions). Though discovering frequent itemsets is useful, it can produce a large amount of spurious patterns. As a result, the user may spend a great amount of time to analyze the itemsets found by a frequent itemset mining algorithm to find truly interesting patterns. Hence, in recent years, a key research topic has emerged which is to discover statistically significant patterns in databases. The most popular model for identifying itemsets that are statistically significant is to discover non-redundant productive itemsets. The state-ofthe-art algorithm to extract this set of patterns is OPUS-Miner. A key drawback of that algorithm is that it is designed to be applied to a static database. Moreover, a second drawback of OPUS-Miner is that it discovers all patterns in a database. In other words, the user cannot search for itemsets containing some specific items. This paper addresses these issues by defining the novel problem of discovering targeted non redundant productive itemsets in dynamic databases. An algorithm named IDPI+ (Interactive Discovery of Productive Itemsets) is presented, storing transactions in a tree structure, which can then be interactively queried to identify productive and non redundant itemsets containing specific items. A structure named Query-Tree is also introduced to process many queries at the same time. Moreover, to handle dynamic databases, efficient transaction insertion and deletion algorithms are provided to update the tree. It was observed in an experimental evaluation on benchmark datasets containing various types of data that IDPI+ can handle thousands of queries per second on a desktop computer. Moreover, it was found that IPDI+ is more than an order of magnitude faster than a baseline algorithm.

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Section: Discussionmentioning

confidence: 99%

Mining Productive Itemsets in Dynamic Databases

Fournier-Viger

et al. 2020

IEEE Access

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“…Another extension of episode mining is top‐k episode mining (Amphawan et al, 2015; Fournier‐Viger et al, 2020). In this task, the user can directly indicate the number of episodes to be found.…”

Section: Extensions Of Traditional Episode Miningmentioning

confidence: 99%

“…However, the main drawback of top‐k episode mining algorithms is that this task is more difficult than traditional FEM if the respective parameters (

k

and the minimum threshold) are set to generate the same number of episodes (Fournier‐Viger et al, 2020). This is because for top‐k episode mining, no assumption can be initially made about the support of the top‐k episodes to reduce the search space.…”

Section: Extensions Of Traditional Episode Miningmentioning

confidence: 99%

A survey of episode mining

Ouarem,

Nouioua,

Fournier‐Viger

2023

WIREs Data Min & Knowl

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Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode‐mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up‐to‐date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules Technologies > Association Rules

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“…It adopts support and temporal confidence metrics to filter out rules with a consequent that is close to the antecedent. Although many studies [1], [6], [21], [26]- [32] have been proposed for FEM, as discussed in the Introduction section, FEM algorithms may discover numerous patterns with low profit, and miss the highly profitable character of low-frequency patterns. In the next subsection, we introduce HUEM.…”

Section: A Frequent Episode Miningmentioning

confidence: 99%

“…To the best of our knowledge, we find that only two algorithms, TKE [32] and TUP [18], are related to the field of top-k episode mining. They discovered frequent episodes and HUEs, respectively.…”

Section: Raising Threshold Strategiesmentioning

confidence: 99%

THUE: Discovering Top-K High Utility Episodes

Wan¹,

Chen²,

Gan³

et al. 2021

Preprint

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Episode discovery from an event is a popular framework for data mining tasks and has many real-world applications. An episode is a partially ordered set of objects (e.g., item, node), and each object is associated with an event type. This episode can also be considered as a complex event sub-sequence. Highutility episode mining is an interesting utility-driven mining task in the real world. Traditional episode mining algorithms, by setting a threshold, usually return a huge episode that is neither intuitive nor saves time. In general, finding a suitable threshold in a pattern-mining algorithm is a trivial and time-consuming task. In this paper, we propose a novel algorithm, called Top-K High Utility Episode (THUE) mining within the complex event sequence, which redefines the previous mining task by obtaining the k highest episodes. We introduce several threshold-raising strategies and optimize the episode-weighted utilization upper bounds to speed up the mining process and effectively reduce the memory cost. Finally, the experimental results on both reallife and synthetic datasets reveal that the THUE algorithm can offer six to eight orders of magnitude running time performance improvement over the state-of-the-art algorithm and has low memory consumption.

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TKE: Mining Top-K Frequent Episodes

Cited by 24 publications

References 27 publications

Mining Productive Itemsets in Dynamic Databases

Mining Productive Itemsets in Dynamic Databases

A survey of episode mining

THUE: Discovering Top-K High Utility Episodes

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