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High average-utility sequential pattern mining based on uncertain databases

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Abstract

The emergence and proliferation of the internet of things (IoT) devices have resulted in the generation of big and uncertain data due to the varied accuracy and decay of sensors and their different sensitivity ranges. Since data uncertainty plays an important role in IoT data, mining the useful information from uncertain dataset has become an important issue in recent decades. Past works focus on mining the high sequential patterns from the uncertain database. However, the utility of a derived sequence increases along with the size of the sequence, which is an unfair measure to evaluate the utility of a sequence since any combination of a high-utility sequence will also be the high-utility sequence, even though the utility of a sequence is merely low. In this paper, we address the limitation of the previous potential high-utility sequential pattern mining and present a potentially high average-utility sequential pattern mining framework for discovering the set of potentially high average-utility sequential patterns (PHAUSPs) from the uncertain dataset by considering the size of a sequence, which can provide a fair measure of the patterns than the previous works. First, a baseline potentially high average-utility sequential pattern algorithm and three pruning strategies are introduced to completely mine the set of the desired PHAUSPs. To reduce the computational cost and accelerate the mining process, a projection algorithm called PHAUP is then designed, which leads to a reduction in the size of candidates of the desired patterns. Several experiments in terms of runtime, number of candidates, memory overhead, number of discovered pattern, and scalability are then evaluated on both real-life and artificial datasets, and the results showed that the proposed algorithm achieves promising performance, especially the PHAUP approach.

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Abbreviations

ARM:

Association rule mining

auub :

Average-utility upper-bound value

AU list:

Average-utility list

FIM:

Frequent itemset mining

HAUIM:

High average-utility itemset mining

HAUIs:

High average-utility itemsets

HTWUIs:

High transaction-weighted utilization itemsets

HUIM:

High-utility itemset mining

HUIs:

High-utility itemsets

HUSPs:

High-utility sequential patterns

HUSPM:

High-utility sequential pattern mining

PHAUSPM:

Potential high average-utility sequential pattern mining

PHAUSPs:

Potential high average-utility sequential patterns

PHAUB:

The designed baseline algorithm

PHAUP:

The designed projection-based algorithm

PHAUUBDC:

Potential high average-utility upper-bound downward closure

PHAUUBSPs:

Potential high average-utility upper-bound sequential patterns

PHUSPs:

Potential high-utility sequential patterns

SPM:

Sequential pattern mining

SWDC:

Sequential weighted downward closure

suub :

Sequence utility upper-bound value

TWU:

Transaction-weighted utility

TWDC:

Transaction-weighted down closure

UFIM:

Frequent itemset mining on uncertain databases

UFIs:

Frequent itemsets in uncertain databases

\( \mu \) :

Minimum expected support threshold

\( \delta \) :

Minimum high average-utility threshold

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Authors and Affiliations

  1. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Ting Li

  3. Department of Computer Science, California State University, Fresno, USA

    Matin Pirouz

  4. School of Agricultural, Computational and Environmental Sciences, University of Southern Queesland, Toowoomba, QLD, Australia

    Ji Zhang

  5. School of Natural Sciences and Humanities, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

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  1. Jerry Chun-Wei Lin
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  4. Ji Zhang
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  5. Philippe Fournier-Viger
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Correspondence to Jerry Chun-Wei Lin.

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Lin, J.CW., Li, T., Pirouz, M. et al. High average-utility sequential pattern mining based on uncertain databases. Knowl Inf Syst 62, 1199–1228 (2020). https://doi.org/10.1007/s10115-019-01385-8

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