Permutation Strategies for Mining Significant Sequential Patterns

Tonon, Andrea; Vandin, Fabio

doi:10.1109/icdm.2019.00169

“…We point interested reader to the survey [7] and the recent works [10][11][12]. Few works have been proposed to mine statistically significant sequential patterns [13][14][15]. These methods are orthogonal to our approach, which focuses on finding patterns whose frequencies with respect to (w.r.t.)…”

Section: Related Workmentioning

confidence: 99%

gRosSo: mining statistically robust patterns from a sequence of datasets

Tonon

¹

,

Vandin

²

2022

Knowl Inf Syst

Self Cite

1

0

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Pattern mining is a fundamental data mining task with applications in several domains. In this work, we consider the scenario in which we have a sequence of datasets generated by potentially different underlying generative processes, and we study the problem of mining statistically robust patterns, which are patterns whose probabilities of appearing in transactions drawn from such generative processes respect well-defined conditions. Such conditions define the patterns of interest, describing the evolution of their probabilities through the datasets in the sequence, which may, for example, increase, decrease, or stay stable, through the sequence. Due to the stochastic nature of the data, one cannot identify the exact set of the statistically robust patterns by analyzing a sequence of samples, i.e., the datasets, taken from the generative processes, and has to resort to approximations. We then propose gRosSo, an algorithm to find rigorous approximations of the statistically robust patterns that do not contain false positives or false negatives with high probability. We apply our framework to the mining of statistically robust sequential patterns and statistically robust itemsets. Our extensive evaluation on pseudo-artificial and real data shows that gRosSo provides high-quality approximations for the problem of mining statistically robust sequential patterns and statistically robust itemsets.

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“…These features however might not be the same for any possible input instance and -as also pointed out in [1] -saliency maps can often be noisy and not always represent the behavior of a neural model. For this reason, we propose to apply a further selection step to prune less reliable feature groups similarly to what proposed in [18] where the authors compute the statistical significance of groups of items by comparing their frequency of occurrence in real data to the one in randomly generated datasets. We compute K random sets of saliency maps, each of the same cardinality of the experimental dataset employed.…”

Section: Neural Modelmentioning

confidence: 99%

Neural Feature Selection for Learning to Rank

Purpura

¹

,

Buchner

²

,

Silvello

³

et al. 2021

Lecture Notes in Computer Science

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LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neural LETOR approaches have become a competitive alternative to traditional ones like LambdaMART. However, neural architectures performance grew proportionally to their complexity and size. This can be an obstacle for their adoption in large-scale search systems where a model size impacts latency and update time. For this reason, we propose an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up to 60% without affecting the system performance. This approach also allows to reduce a LETOR model complexity and, therefore, its training and inference time up to 50%.

show abstract

“…These features however might not be the same for any possible input instance and -as also pointed out in [1] -saliency maps can often be noisy and not always represent the behavior of a neural model. For this reason, we propose to apply a further selection step to prune less reliable feature groups similarly to what proposed in [18] where the authors compute the statistical significance of groups of items by comparing their frequency of occurrence in real data to the one in randomly generated datasets.…”

Section: Neural Modelmentioning

confidence: 99%

Neural Feature Selection for Learning to Rank

Purpura¹,

Buchner²,

Silvello³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neural LETOR approaches have become a competitive alternative to traditional ones like LambdaMART. However, neural architectures performance grew proportionally to their complexity and size. This can be an obstacle for their adoption in large-scale search systems where a model size impacts latency and update time. For this reason, we propose an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up to 60% without affecting the system performance. This approach also allows to reduce a LETOR model complexity and, therefore, its training and inference time up to 50%.

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Permutation Strategies for Mining Significant Sequential Patterns

Cited by 23 publications

References 21 publications

gRosSo: mining statistically robust patterns from a sequence of datasets

gRosSo: mining statistically robust patterns from a sequence of datasets

Neural Feature Selection for Learning to Rank

Neural Feature Selection for Learning to Rank

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