Interactive Data Exploration Using Pattern Mining

Leeuwen, Matthijs van

doi:10.1007/978-3-662-43968-5_9

Cited by 40 publications

(21 citation statements)

References 27 publications

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“…Nevertheless, most works concerning user-specific pattern interestingness measures and interactive pattern mining are fairly recent. 26 These approaches can be divided into three groups:…”

Section: Related Workmentioning

confidence: 99%

Interactive Learning of Pattern Rankings

Dzyuba

Leeuwen

Nijssen

et al. 2014

Int. J. Artif. Intell. Tools

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Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts.To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to rank small sets of patterns, while a ranking function is inferred from this feedback by preference learning techniques. Moreover, we propose a number of active learning heuristics to minimize the effort required from the user, while ensuring that accurate rankings are obtained. We show how the learned ranking functions can be used to mine new, more interesting patterns.We demonstrate two concrete instances of our framework for two different pattern mining tasks, frequent itemset mining and subgroup discovery. We empirically evaluate the capacity of the algorithm to learn pattern rankings by emulating users. Experiments demonstrate that the system is able to learn accurate rankings, and that the active learning heuristics help reduce the required user effort. Furthermore, using the learned ranking functions as search heuristics allows discovering patterns of higher quality than those in the initial set. This shows that machine learning techniques in general, and active preference learning in particular, are promising building blocks for interactive data mining systems.

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“…Nevertheless, most works concerning user-specific pattern interestingness measures and interactive pattern mining are fairly recent. 26 These approaches can be divided into three groups:…”

Section: Related Workmentioning

confidence: 99%

Interactive Learning of Pattern Rankings

Dzyuba

Leeuwen

Nijssen

et al. 2014

Int. J. Artif. Intell. Tools

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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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“…There are some recent works in interactive knowledge discovery, which are not necessarily frequent pattern mining. Examples include mining geospatial redescriptions , and subgroup discovery . Broadly speaking, these works fall in the category of interactive pattern discovery, but the problem definition and the scope of these works are very specific.…”

Section: Related Workmentioning

confidence: 99%

“…In refs. , the authors present a framework that utilizes user's feedback and devises a search procedure for finding subgroups.…”

Section: Related Workmentioning

confidence: 99%

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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Interactive Data Exploration Using Pattern Mining

Cited by 40 publications

References 27 publications

Interactive Learning of Pattern Rankings

Interactive Learning of Pattern Rankings

Mining Redescriptions with Siren

Interactive knowledge discovery from hidden data through sampling of frequent patterns

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