Sequential Pattern Sampling with Norm Constraints

Diop, Lamine; Diop, Cheikh Talibouya; Giacometti, Arnaud; Li, Dominique; Soulet, Arnaud

doi:10.1109/icdm.2018.00024

Cited by 14 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To consider another measure, Moens and Boley had to design a new method [28]. Considering sequences, Diop et al proposed an approach which guarantees that the probability of sampling a sequential pattern is proportional to its frequency [12,13]. It focuses on the frequency measure only.…”

Section: Heuristic Methodsmentioning

confidence: 99%

Anytime mining of sequential discriminative patterns in labeled sequences

et al. 2020

View full text Add to dashboard Cite

Section: Heuristic Methodsmentioning

confidence: 99%

Anytime mining of sequential discriminative patterns in labeled sequences

et al. 2020

View full text Add to dashboard Cite

“…However this method only works on those measures. Diop et al proposed an approach which guarantees that the probability of sampling a sequential pattern is proportional to its frequency [8]. It focuses on the frequency measure only.…”

Section: B Heuristic Methodsmentioning

confidence: 99%

“…Heuristic methods for sequential data have not yet attracted much attention. [8] introduced a sampling approach that is dedicated to the frequency measure only. A promising recent proposal is [9]: the sampling method misère can be used for any quality measure when exploiting sequences of events.…”

Section: Introductionmentioning

confidence: 99%

SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences

Mathonat¹,

Nurbakova²,

Boulicaut³

et al. 2019

2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

View full text Add to dashboard Cite

It is extremely useful to exploit labeled datasets not only to learn models but also to improve our understanding of a domain and its available targeted classes. The so-called subgroup discovery task has been considered for a long time. It concerns the discovery of patterns or descriptions, the set of supporting objects of which have interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for transactional data, discovering subgroups within labeled sequential data and thus searching for descriptions as sequential patterns has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. We propose the algorithm SeqScout to discover interesting subgroups (w.r.t. a chosen quality measure) from labeled sequences of itemsets. This is a new sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. It is an anytime algorithm that, for a given budget, finds a collection of local optima in the search space of descriptions and thus subgroups. It requires a light configuration and it is independent from the quality measure used for pattern scoring. Furthermore, it is fairly simple to implement. We provide qualitative and quantitative experiments on several datasets to illustrate its added-value.

show abstract

“…For this reason, we prefer to adopt a multi-step random method. This type of method has already been used for several interestingness measures (e.g., support or area [3] or exceptional measure [12]) and several data types like sequential data [5]. Nevertheless, the context of distributed databases is an orthogonal challenge.…”

Section: Related Workmentioning

confidence: 99%

“…More precisely, given a database D and a sample S, the approximated FPOF of a transaction t ∈ D is We see that without constraint or with a large value for M (≥ 5), a large majority of FPOF values are equal to zero, which implies that it is impossible to distinguish outliers from normal entities. Indeed, without constraint, FPOF suffers from the long tail problem [5]. Therefore, the use of a maximum length constraint is crucial for detecting outliers by means of FPOF.…”

Section: Efficiency and Robustness Of Ddsamplingmentioning

confidence: 99%

Pattern Sampling in Distributed Databases

Diop

Giacometti

et al. 2020

Advances in Databases and Information Systems

Self Cite

View full text Add to dashboard Cite

Many applications rely on distributed databases. However, only few discovery methods exist to extract patterns without centralizing the data. In fact, this centralization is often less expensive than the communication of extracted patterns from the different nodes. To circumvent this difficulty, this paper revisits the problem of pattern mining in distributed databases by benefiting from pattern sampling. Specifically, we propose the algorithm DDSampling that randomly draws a pattern from a distributed database with a probability proportional to its interest. We demonstrate the soundness of DDSampling and analyze its time complexity. Finally, experiments on benchmark datasets highlight its low communication cost and its robustness. We also illustrate its interest on real-world data from the Semantic Web for detecting outlier entities in DBpedia and Wikidata.

show abstract

Sequential Pattern Sampling with Norm Constraints

Cited by 14 publications

References 22 publications

Anytime mining of sequential discriminative patterns in labeled sequences

Anytime mining of sequential discriminative patterns in labeled sequences

SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences

Pattern Sampling in Distributed Databases

Contact Info

Product

Resources

About