One click mining

Boley, Mario; Mampaey, Michael; Kang, Bo; Tokmakov, Pavel; Wrobel, Stefan

doi:10.1145/2501511.2501517

Cited by 45 publications

(12 citation statements)

References 20 publications

(10 reference statements)

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“…Later, Boley et al (2013) and Dzyuba et al (2014) simultaneously (and independently) developed methods to learn pattern rankings using techniques from preference learning. Boley et al also presented a working system for what they called 'one-click-mining', in which the preferences of the user for certain algorithms and patterns are learned.…”

Section: Instant and Interactive Pattern Miningmentioning

confidence: 99%

Subjective interestingness of subgraph patterns

et al. 2016

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The utility of a dense subgraph in gaining a better understanding of a graph has been formalised in numerous ways, each striking a different balance between approximating actual interestingness and computational efficiency. A difficulty in making this trade-off is that, while computational cost of an algorithm is relatively well-defined, a pattern's interestingness is fundamentally subjective. This means that this latter aspect is often treated only informally or neglected, and instead some form of density is used as a proxy. We resolve this difficulty by formalising what makes a dense subgraph pattern interesting to a given user. Unsurprisingly, the resulting measure is dependent on the prior beliefs of the user about the graph. For concreteness, in this paper we consider two cases: one case where the user only has a belief about the overall density of the graph, and another case where the user has prior beliefs about the degrees of the vertices. Furthermore, we illustrate how the resulting interestingness measure is different from previous proposals. We also propose effective exact and approximate algorithms for mining the most interesting dense subgraph according to the proposed measure. Usefully, the proposed interestingness measure and approach lend themselves well to iterative dense subgraph discovery. Contrary to most existing approaches, Learn (2016) 105:41-75 our method naturally allows subsequently found patterns to be overlapping. The empirical evaluation highlights the properties of the new interestingness measure given different prior belief sets, and our approach's ability to find interesting subgraphs that other methods are unable to find.

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Section: Instant and Interactive Pattern Miningmentioning

confidence: 99%

Subjective interestingness of subgraph patterns

et al. 2016

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“…We could not perform any accuracy experiment of the one‐click method by ref. because, in that paper, the authors did not include any empirical experimentation to measure accuracy/efficiency of their method.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Few methods of interactive mining exist that are not constraint‐guided—probably the most similar to our work are refs. , . However, our method differs from ref.…”

Section: Related Workmentioning

confidence: 92%

“…Second, our method uses binary feedback instead of rank order feedback that the above method adopts—the latter puts more burden on the user, and in the case of a hidden dataset, the user may have little information to rank the patterns properly. Boley et al's work also considers learning a ranking function of a user over the frequent patterns' space. They do overcome the constraint of mining all frequent patterns upfront, but this work also requires the user to provide a complete ordering among released patterns—a difficult task when the dataset is hidden.…”

Section: Related Workmentioning

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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“…Recently, research interest has arisen at the crossroads of interactive data mining and pattern selection [Boley et al 2013;van Leeuwen 2014;Miettinen 2014].…”

Section: Pattern Selection and Associated Pitfallsmentioning

confidence: 99%

Mining Redescriptions with Siren

Galbrun

Miettinen

2018

ACM Trans. Knowl. Discov. Data

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In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions both in terms of the fauna that inhabits them and of their bioclimatic conditions. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining.If a domain expert wants to use redescription mining in his research, merely being able to find redescriptions is not enough. He must also be able to understand the redescriptions found, adjust them to better match his domain knowledge, test alternative hypotheses with them, and guide the mining process towards results he considers interesting. To facilitate these goals, we introduce Siren, an interactive tool for mining and visualizing redescriptions.Siren allows to obtain redescriptions in an anytime fashion through efficient, distributed mining, to examine the results in various linked visualizations, to interact with the results either directly or via the visualizations, and to guide the mining algorithm toward specific redescriptions. In this paper, we explain the features of Siren and why they are useful for redescription mining. We also propose two novel redescription mining algorithms that improve the generalizability of the results compared to the existing ones.

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One click mining

Cited by 45 publications

References 20 publications

Subjective interestingness of subgraph patterns

Subjective interestingness of subgraph patterns

Interactive knowledge discovery from hidden data through sampling of frequent patterns

Mining Redescriptions with Siren

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