Linear space direct pattern sampling using coupling from the past

Boley, Mario; Moens, Sandy; Gärtner, Thomas

doi:10.1145/2339530.2339545

Cited by 33 publications

(67 citation statements)

References 21 publications

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“…In recent years, various other works also follow a similar approach—notable among these are refs. . In ref.…”

Section: Related Workmentioning

confidence: 99%

“…An improved sampling framework is proposed in ref. , which uses coupling from the past (CFTP) technique. CFTP helps to avoid super‐linear preprocessing and memory requirements, and it can simulate more complex distributions.…”

Section: Related Workmentioning

confidence: 99%

“…Our solution for frequent pattern mining from hidden data utilizes sampling of patterns from the frequent pattern space. In the literature, there exist several pattern sampling algorithms , but they do not fulfill the purpose of our task. The major difference of these algorithms with ours is that the sampling distribution of our method changes through user interaction, whereas in the above‐cited works the sampling distribution remains fixed throughout the sampling session.…”

Section: Introductionmentioning

confidence: 99%

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Interactive knowledge discovery from hidden data through sampling of frequent patterns

Bhuiyan

Hasan

2016

Statistical Analysis

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In real life, many important datasets are not publicly accessible due to various reasons, including privacy protection and maintenance of business competitiveness. However, Knowledge discovery and pattern mining from these datasets can bring enormous benefit both to the data owner and the external entities. In this paper, we propose a novel solution for this task, which is based on Markov chain Monte Carlo (MCMC) sampling of frequent patterns. Instead of returning all the frequent patterns, the proposed paradigm sends back a small set of randomly selected patterns so that the confidentiality of the dataset can be maintained. Our solution also allows interactive sampling, so that the sampled patterns can fulfill the user's requirement effectively. We show experimental results from several real‐life datasets to validate the capability and usefulness of our solution. In particular, we show examples that by using our proposed solution, an eCommerce marketplace can allow pattern mining on user session data without disclosing the data to the public; such a mining paradigm can help the sellers in the marketplace, which eventually can boost the market's own revenue. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

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“…In recent years, various other works also follow a similar approach—notable among these are refs. . In ref.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interactive knowledge discovery from hidden data through sampling of frequent patterns

Bhuiyan

Hasan

2016

Statistical Analysis

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“…Also for itemsets, Boley et al [6] developed a general sampling framework for generating small collections of patterns from a user-defined distribution. In this work a user constructs a distribution over the complete pattern space by defining singleton preference weights and a multiplicative or additive evaluation scheme.…”

Section: Related Workmentioning

confidence: 99%

Randomly sampling maximal itemsets

Moens

Goethals

2013

Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

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Pattern mining techniques generally enumerate lots of uninteresting and redundant patterns. To obtain less redundant collections, techniques exist that give condensed representations of these collections. However, the proposed techniques often rely on complete enumeration of the pattern space, which can be prohibitive in terms of time and memory. Sampling can be used to filter the output space of patterns without explicit enumeration. We propose a framework for random sampling of maximal itemsets from transactional databases. The presented framework can use any monotonically decreasing measure as interestingness criteria for this purpose. Moreover, we use an approximation measure to guide the search for maximal sets to different parts of the output space. We show in our experiments that the method can rapidly generate small collections of patterns with good quality. The sampling framework has been implemented in the interactive visual data mining tool called MIME 1 , as such enabling users to quickly sample a collection of patterns and analyze the results.

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“…Direct pattern sampling produces random pattern collections as the outcome of fast appropriately biased random experiments without constructing auxiliary parts of the pattern space (see Boley et al [2012] from which we also use the complementary pattern sampling library 1 ). Direct pattern sampling produces random pattern collections as the outcome of fast appropriately biased random experiments without constructing auxiliary parts of the pattern space (see Boley et al [2012] from which we also use the complementary pattern sampling library 1 ).…”

Section: Prototype Configurationmentioning

confidence: 99%

One click mining

Boley

Mampaey

Kang

et al. 2013

Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

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It is known that productive pattern discovery from data has to interactively involve the user as directly as possible. State-of-the-art toolboxes require the specification of sophisticated workflows with an explicit selection of a data mining method, all its required parameters, and a corresponding algorithm. This hinders the desired rapid interactionespecially with users that are experts of the data domain rather than data mining experts. In this paper, we present a fundamentally new approach towards user involvement that relies exclusively on the implicit feedback available from the natural analysis behavior of the user, and at the same time allows the user to work with a multitude of pattern classes and discovery algorithms simultaneously without even knowing the details of each algorithm. To achieve this goal, we are relying on a recently proposed co-active learning model and a special feature representation of patterns to arrive at an adaptively tuned user interestingness model. At the same time, we propose an adaptive time-allocation strategy to distribute computation time among a set of underlying mining algorithms. We describe the technical details of our approach, present the user interface for gathering implicit feedback, and provide preliminary evaluation results.

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Linear space direct pattern sampling using coupling from the past

Cited by 33 publications

References 21 publications

Interactive knowledge discovery from hidden data through sampling of frequent patterns

Interactive knowledge discovery from hidden data through sampling of frequent patterns

Randomly sampling maximal itemsets

One click mining

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