PRIIME: A generic framework for interactive personalized interesting pattern discovery

Bhuiyan, Mansurul; Hasan, Mohammad Al

doi:10.1109/bigdata.2016.7840653

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…The central idea behind these algorithms is to alternate between mining and learning. Priime [3] focuses on advanced feature construction for interactive mining of structured data, e.g., sequences or graphs.…”

Section: Related Workmentioning

confidence: 99%

Learning What Matters – Sampling Interesting Patterns

Dzyuba

Leeuwen

2017

Advances in Knowledge Discovery and Data Mining

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In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the stateof-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods. This document is an extended version of a conference publication [14].

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Section: Related Workmentioning

confidence: 99%

Learning What Matters – Sampling Interesting Patterns

Dzyuba

Leeuwen

2017

Advances in Knowledge Discovery and Data Mining

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“…In [14], the authors proposed a user-centric generic framework (PRIIME) to learn ranking functions. To achieve this, regression techniques based on neural networks were adopted.…”

Section: Related Workmentioning

confidence: 99%

Boosting the Learning for Ranking Patterns

et al. 2023

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Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user mistakes, compared to state-of-the-art approaches.

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“…The same technique is also used in [5], but the learned function is exploited for patterns sampling. In [14], regression techniques are adopted to learn ranking functions.…”

Section: Related Workmentioning

confidence: 99%

Boosting the Learning for Ranking Patterns

Belmecheri

Aribi

Lazaar

et al. 2022

Preprint

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Discovering relevant patterns for a particular user remains a challenging tasks in data mining. Several approaches have been proposed to learn user-specific pattern ranking functions. These approaches generalize well, but at the expense of the running time. On the other hand, several measures are often used to evaluate the interestingness of patterns, with the hope to reveal a ranking that is as close as possible to the user-specific ranking. In this paper, we formulate the problem of learning pattern ranking functions as a multicriteria decision making problem. Our approach aggregates different interestingness measures into a single weighted linear ranking function, using an interactive learning procedure that operates in either passive or active modes. A fast learning step is used for eliciting the weights of all the measures by mean of pairwise comparisons. This approach is based on Analytic Hierarchy Process (AHP), and a set of user-ranked patterns to build a preference matrix, which compares the importance of measures according to the user-specific interestingness. A sensitivity based heuristic is proposed for the active learning mode, in order to insure high quality results with few user ranking queries. Experiments conducted on well-known datasets show that our approach significantly reduces the running time and returns precise pattern ranking, while being robust to user-error compared with state-of-the-art approaches.

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PRIIME: A generic framework for interactive personalized interesting pattern discovery

Cited by 7 publications

References 22 publications

Learning What Matters – Sampling Interesting Patterns

Learning What Matters – Sampling Interesting Patterns

Boosting the Learning for Ranking Patterns

Boosting the Learning for Ranking Patterns

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