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REVIEW 4 major objections 6 minor 75 references

SHARQ: Explainability Framework for Association Rules on Relational Data

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Exact, fast Shapley scores for association rule elements

desk verdict New Shapley-based measure for association rule elements with an exact linear-time shortcut; the math holds under a stated input assumption, but the paper needs cleanup on inconsistent numbers and example scaling. read the letter →

arxiv 2412.18522 v1 pith:7E3CSNOH submitted 2024-12-24 cs.DB

Hadar Ben-Efraim , Susan B. Davidson , Amit Somech This is my paper · ORCID
classification cs.DB
keywords associationrulesShapleyvalueexplainabilityelementimportanceruleattributerule-setinterestingnessexactalgorithm
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a data element's contribution to a set of mined association rules can be measured exactly by a Shapley-value score, called SHARQ, and that this otherwise exponential computation can be made practical. The score works by asking how a rule set's aggregate interestingness changes when an element is added to any attribute-disjoint coalition of elements. If the paper is right, analysts can rank elements, flag redundant rules, and rank attributes in rule sets containing hundreds of thousands of rules, not just hand-picked examples. The key practical claim is that the exact score can be computed in time that is practically linear in the number of rules.

What carries the argument

The load-bearing object is the pruned coalition set $\mathcal{C}^*(e,E)=\{S: \exists r\in R,\ S\subseteq E(r),\ |S|\ge|E(r)|-1,\ attr(e)\notin attr(S)\}$, paired with a coalition-to-rules index built in one pass. This restricts the Shapley sum to coalitions that are either a full rule's elements or one element short of a rule, because only those can change the utility difference $I(R_{S\cup\{e\}})-I(R_S)$. The index stores, for each coalition $S$, the rules whose elements equal $S$, so utility lookups are $O(1)$ and the factorial Shapley weights are applied only to surviving coalitions.

What would settle it

Take a rule set containing one rule with two elements of the same attribute, compute the original SHARQ definition directly, and compare with the optimized SHARQ*; because $\mathcal{C}^*$ and the proof rely on distinct attributes inside rules, a nonzero difference (or an undefined coalition) would refute Proposition 3.1.

Watch

Extended reading notes

Core claim

The central claim is Proposition 3.1: for every element $e$, the optimized formula $\mathrm{SHARQ}^*_{(E,R)}(e)$ equals the original definition $\mathrm{SHARQ}_{(E,R)}(e)$. The paper argues that any coalition of elements that is not contained in some rule, or is two or more elements short of a rule, contributes zero to the Shapley sum, because both the coalition and the coalition with $e$ match no rules; hence those coalitions can be pruned without changing the result. The resulting single-element algorithm runs in $O(|R|\cdot\tau\cdot(\tau+\gamma))$ time, essentially $O(|R|\cdot\tau^2)$ and called practically linear in the number of rules $|R|$, where $\tau$ is maximum rule size and $\gamma\le1$ is the fraction of rules lacking $e$'s attribute. The paper also claims a multi-element algorithm amortizes coalition generation across elements sharing an attribute, giving an average 13.8x speedup over sequential single-element runs in experiments.

Load-bearing premise

The framework assumes that every rule contains at most one element per attribute, i.e., a rule never lists two different values of the same column; if a rule ever does, the definition of valid coalitions, the pruning set, and the exactness proof all stop working.

Editorial extensions

If this is right

  • Exact element-level SHARQ scores become computable for large mined rule sets: average 6.6-6.8 seconds per element versus hours or days for the naive calculation.
  • Element importance rankings can differentiate elements that generic measures like I_TOP and Influence cannot, as in the running example where one element scores -0.6 and two others score 4.6.
  • Rule-level R-SHARQ flags redundant rules: in the Adult use case, 95% of rules fall below the demonstrated threshold, leaving 3,140 non-redundant rules.
  • Attribute-level A-SHARQ gives a global view of which columns drive rule generation, supporting dimensionality reduction.
  • Direct Shapley approximations, especially kernel weighting, preserve element rankings (p@10 0.92, rank correlation 0.93) better than generic contribution measures, with comparable running time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the disjoint-attribute assumption could be relaxed to allow multi-valued attributes, the same coalition-pruning idea would need a corrected validity condition; the paper does not explore this, but the exactness proof's reliance on non-repeated attributes pinpoints where the change would land.
  • The normalized rule-level score R-SHARQ is demonstrated at a threshold of 0.21, which reduces an 84,479-rule set to 3,140 rules; a natural next step the paper leaves implicit is a principled way to set that threshold from a false-discovery or coverage target.
  • The multi-element algorithm's amortization by attribute suggests SHARQ scores could be maintained incrementally when rules are added or removed; the paper names updates as future work but gives no algorithm.
  • Because kernel-weighted permutation sampling preserves element ranking almost as well as exact SHARQ at comparable cost, a practical deployment could use the approximation to screen elements and exact SHARQ only for the top candidates; the paper reports the quality numbers but does not advocate this pipeline.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces SHARQ, a Shapley-value-based measure of a data element's contribution to the interestingness of a set of association rules. It defines SHARQ over valid coalitions of elements with disjoint attributes, proves an equivalent optimized formula SHARQ* (Proposition 3.1), and presents a single-element algorithm with cost O(|R|·τ·(τ+γ)), called practically linear in the rule count, plus a multi-element variant that amortizes coalition construction over elements sharing an attribute. The paper also defines rule-level and attribute-level importance scores built from SHARQ and reports experiments on rule sets mined from several datasets, claiming that SHARQ* is orders of magnitude faster than naive computation and that multi-element SHARQ* gives roughly a 12–14× speedup over sequential computation. The central theoretical claim is that SHARQ* computes exactly the SHARQ score while avoiding the exponential enumeration of all valid coalitions.

Significance. If the correctness and complexity claims hold, SHARQ is a useful explainability primitive for association rules: it gives exact Shapley-based element attributions in a setting where naive enumeration is infeasible, and the rule- and attribute-level aggregates provide concrete downstream use cases. The paper's strengths include a direct equivalence proof of SHARQ* (Proposition 3.1), a complexity analysis consistent with the algorithm pseudocode, and an evaluation that compares coalition counts and running times on diverse rule sets rather than relying on fitted parameters. The benchmark of mined rule sets is a useful contribution, though the reported instance counts are currently inconsistent. The main correctness concern is that the exactness proof is conditional on an unstated and unchecked assumption about duplicate attributes in rules; the main empirical concern is a tangle of numerical inconsistencies in the example, the redundancy use case, and the evaluation-set size.

major comments (4)
  1. [Abstract and Section 5.1] The number of evaluation instances is inconsistent: the abstract and the contributions list say 45 instances, while Section 5.1 first says '45 different rule mining results' and then says the pipelines 'generated a total of 67 distinct rules sets'; Table 5 lists dataset counts summing to 67; and Section 5.3 refers both to 'our 45⟨D,R⟩ evaluation instances' and to 'all 67 instances.' Because the averaged results (e.g., 6.6s per element, 13.8X multi-element speedup) depend on which set is used, this inconsistency must be fixed and the per-dataset counts reconciled before the experimental claims can be evaluated.
  2. [Section 2.2 and Proposition 3.1] The exactness of SHARQ* is conditional on the assumption, stated in Section 2.2, that 'a rule never contains two elements with the same attribute.' Equation (3) sums over C*(e,E), which is defined in Equations (1)–(2) without the |attr(S)|=|S| condition used in C(e,E). Under the stated assumption the proof is sound, and for rules mined from tuples with positive support the assumption follows from the tuple model. However, the paper does not state that derivation, does not check the condition when rules are supplied from an external mining pipeline, and does not discuss what happens when the condition fails (e.g., multi-valued attributes or overlapping bins). Please state the assumption as an explicit input contract, add a validation step, and discuss the limitation; otherwise Proposition 3.1 is not a guarantee for all inputs accepted by the algorithms.
  3. [Section 2.1 and Example 2.1 / Table 2] The running example's IS scores contradict the formal IS definition. Section 2.1 defines IS(r)=sqrt(support(r)·lift(r)); for r1 this is sqrt(0.2·5.25)≈1.02, yet Table 2 reports 'IS score (×10^2)' = 105 and Example 2.1 uses values 1.05 and 1.02. The reported numbers match support·lift (not its square root), and the example's calculation '3!(6−3−1)!/6!·(1.05−1.02)=0.05' is also arithmetically wrong: the factorial ratio is 1/60 and the difference is 0.03, giving 0.0005 (or 0.05 only if the difference is 3). This inconsistency undermines the illustrative example used to explain SHARQ; please correct the formula, the table values, or the example so that all numbers are internally consistent.
  4. [Section 4.2] The redundancy use case contains an arithmetic error. The text states that at a rule-level SHARQ threshold of 0.21, 'a total of 68250 (95%) rules fall below the threshold' and that eliminating them leaves '3140 important, non-redundant rules.' With 84,479 total rules, 84,479−68,250=16,229 (not 3,140), and 68,250/84,479≈80.8% (not 95%). The claimed pruning effectiveness of the rule-importance use case is therefore not supported by the reported numbers; please correct the counts or the threshold and recompute the stated percentages.
minor comments (6)
  1. [Section 5.1] The text says the evaluation set is built from 'four underlying datasets' but then lists six datasets (Adult, Spotify, Flights, Isolet, Covid-19, Adult-ACS); please correct the number.
  2. [Section 3.1] In the 'Number of Coalitions' paragraph, 'each subset E⊆E(r) of size≥|E(r)|' should read 'each subset S⊆E(r) of size |S|≥|E(r)|−1'; the current wording is confusing.
  3. [Section 2.2] In the sentence 'Given an element e, and a set of elements E∈E(D)', the symbol E should be a subset of E(D), not an element of it; please write E⊆E(D).
  4. [Section 4.3] In the definition of A-SHARQ, the denominator is written as 'E_a', a set; it should be the number of elements of attribute a that appear in at least one rule, e.g., |E_a ∩ E(R)|.
  5. [General] The paper uses 'Shapely' in several places (e.g., the abstract); it should be 'Shapley.'
  6. [Section 5.3] The per-dataset speedup factors listed in the text (13.6X, 17.5X, 20.4X, 16.2X, 8X, 12.6X) do not average to the stated 13.8X; please verify the numbers or the averaging method.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Proposition 3.1 is proved directly from the SHARQ definition, and the efficiency claims are analytic or empirically evaluated rather than fitted.

full rationale

The paper's central technical claim is that the pruned formula SHARQ* equals the original SHARQ definition. That claim is supported by a direct proof from the definitions of C(e,E) and C*(e,E), not by fitting or by importing a result from prior work. The proof explicitly uses the stated assumption that rules never contain two elements with the same attribute; if that assumption is violated, Proposition 3.1 can fail, but that is a robustness/correctness caveat rather than circularity. The complexity analysis derives an O(|R| tau (tau + gamma)) bound from the algorithm's loops, and the experimental sections measure coalition counts and running times on independently generated rule sets; no fitted parameter is relabeled as a prediction. Some cited works include a co-author's prior paper (e.g., reference [20]) and standard Shapley references, but these are contextual and not load-bearing: the equivalence proof and the algorithms do not depend on any self-citation. The rule-importance and attribute-importance scores are additional definitions built on SHARQ, not circular attempts to derive SHARQ from them. Overall, the derivation chain is self-contained and no input is equivalent to the output by construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No new physical or data entities are postulated. SHARQ is a derived score, not an entity. The central claim is algorithmic, so the ledger mainly captures background assumptions about relational rules and practical rule-set shapes.

assumptions (4)
  • domain assumption Rules never contain two elements with the same attribute.
    Stated in Section 2.2 and used to define valid coalitions C(e,E) and the pruned set C*(e,E). Breaks for multi-valued attributes.
  • domain assumption The interestingness of an empty rule set is zero: I(empty set) = 0.
    Stated in Section 2.2 as "Naturally, if R_S is empty then I(R_S) = 0"; required for Proposition 3.1 to drop zero-contribution coalitions.
  • domain assumption Association rule sets mined in practice have small maximum rule length tau and rule count |R| much smaller than |T|^|A|.
    Used to claim O(|R| times tau^2) is "practically linear"; the authors acknowledge worst cases where tau equals |A| and |R| is exponential. Supported only by their own evaluation.
  • domain assumption The score function and aggregation I are fixed and computable in O(1) after indexing.
    The cost analysis assumes O(1) lookup of I(R_S); with set-keyed hash indexes and max aggregation over rule lists, this hides an O(|S|) factor, which is absorbed by tau in the final bound.

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Cite this review

Pith. "Pith review of SHARQ: Explainability Framework for Association Rules on Relational Data." pith.science (2026). https://pith.science/paper/7E3CSNOH

@misc{pith2026241218522,
  author       = {Pith},
  title        = {Pith review of: SHARQ: Explainability Framework for Association Rules on Relational Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7E3CSNOH}},
  note         = {Machine review of arXiv:2412.18522}
}
read the original abstract

Association rules are an important technique for gaining insights over large relational datasets consisting of tuples of elements (i.e. attribute-value pairs). However, it is difficult to explain the relative importance of data elements with respect to the rules in which they appear. This paper develops a measure of an element's contribution to a set of association rules based on Shapley values, denoted SHARQ (ShApley Rules Quantification). As is the case with many Shapely-based computations, the cost of a naive calculation of the score is exponential in the number of elements. To that end, we present an efficient framework for computing the exact SharQ value of a single element whose running time is practically linear in the number of rules. Going one step further, we develop an efficient multi-element SHARQ algorithm which amortizes the cost of the single element SHARQ calculation over a set of elements. Based on the definition of SHARQ for elements we describe two additional use cases for association rules explainability: rule importance and attribute importance. Extensive experiments over a novel benchmark dataset containing 45 instances of mined rule sets show the effectiveness of our approach.

Figures

Figures reproduced from arXiv: 2412.18522 by the authors.

Figure 1
Figure 1. Example SHARQ scores alongside rules statistics, using rules mined from the Adult [1] dataset. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. R-SHARQ rule score examples. rules, spanning 34 unique dataset elements (see our code repository in [52] for full settings and output). Next, we calculate the SHARQ scores for all individual elements in 𝐸 = E (𝑅), i.e., all elements that participate in at least one rule. As briefly introduced in Example 1.3, [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. A-SHARQ attribute importance scores elements. The Relationship column is ranked third, as one of its elements, ’Not-in-Family’, has a low importance score (colored in grey in the figure). Also note that the columns Capital-loss, Capital-gain, Native￾country, and Race obtain the lowest A-SHARQ importance scores. This is because they all contain a single dominant element with a high frequency, yet low importance (see … view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Comparison of naive and optimized approaches Adult Adult-ACS Covid-19 Flights IsoletSpotify Dataset 0 100 200 300 400 Average running time (sec) 354.8 61.0 470.2 43.0 296.7 46.8 25.2 7.2 35.9 2.7 19.0 2.8 (a) Avg. time by dataset 15 20 25 30 35 40 Number of elements 0 …
Figure 5
Figure 5. Figure 5: Comparison between multi-element and sequential approaches number of coalitions as done for the naive SHARQ performance evaluation [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: SHARQ Scores correlation when using alternative interestingness notions. configuration uses the IS measure [67] and 𝐼(𝑅) B 𝑚𝑎𝑥𝑟 ∈𝑅 score(𝑟) for the aggregated interestingness of a rules set, as defined in Sec￾tion 2.1. To gauge this effect, we examine the correlation b…

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Reference graph

Works this paper leans on

75 extracted references · 62 canonical work pages

  1. [1]

    Adults Income Dataset (UCI). 2024. https://archive.ics.uci.edu/ml/datasets/Adult/. (2024)

    work page 2024

  2. [2]

    Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. 1993. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data . 207–216

    1993

  3. [3]

    Rakesh Agrawal, Ramakrishnan Srikant, et al. 1994. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB , Vol. 1215. Citeseer, 487–499

    1994

  4. [4]

    Talha Mahboob Alam, Muhammad Atif Iqbal, Yasir Ali, Abdul Wahab, Safdar Ijaz, Talha Imtiaz Baig, Ayaz Hussain, Muhammad Awais Malik, Muhammad Mehdi Raza, Salman Ibrar, et al. 2019. A model for early prediction of diabetes. Infor- matics in Medicine Unlocked 16 (2019), 100204

    work page 2019

  5. [5]

    Yael Amsterdamer, Daniel Deutch, and Val Tannen. 2011. Provenance for ag- gregate queries. In Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems . 153–164

    work page 2011

  6. [6]

    Zhifeng Bao, Yong Zeng, HV Jagadish, and Tok Wang Ling. 2015. Exploratory keyword search with interactive input. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data . 871–876

    work page 2015

  7. [7]

    Roberto J Bayardo Jr and Rakesh Agrawal. 1999. Mining the most interesting rules. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining . 145–154

    work page 1999

  8. [8]

    Leopoldo Bertossi, Benny Kimelfeld, Ester Livshits, and Mikaël Monet. 2023. The Shapley value in database management. ACM Sigmod Record 52, 2 (2023), 6–17

    work page 2023

Show all 75 references
  1. [9]

    Ramon Bespinyowong, Wei Chen, HV Jagadish, and Yuxin Ma. 2016. ExRank: An exploratory ranking interface. Proceedings of the VLDB Endowment 9, 13 (2016), 1529–1532

    2016

  2. [10]

    Sergey Brin, Rajeev Motwani, and Craig Silverstein. 1997. Beyond market baskets: Generalizing association rules to correlations. In Proceedings of the 1997 ACM SIGMOD international conference on Management of data . 265–276

    1997

  3. [11]

    Sergey Brin, Rajeev Motwani, Jeffrey D Ullman, and Shalom Tsur. 1997. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the 1997 ACM SIGMOD international conference on Management of data . 255–264

    1997

  4. [12]

    Buneman, S

    P. Buneman, S. Khanna, and W.C. Tan. 2001. Why and Where: A Characterization of Data Provenance. In ICDT. 316–330

    2001

  5. [13]

    Varun Chandola and Vipin Kumar. 2007. Summarization–compressing data into an informative representation. Knowledge and Information Systems 12, 3 (2007), 355–378

    2007

  6. [14]

    X Chang, H Li, Y Fu, and D Yang. 2018. Knowledge-Based Error Detection in External Beam Physician Orders Using Association Rules. International Journal of Radiation Oncology, Biology, Physics 102, 3 (2018), S119–S120

    2018

  7. [15]

    Adriane Chapman and HV Jagadish. 2009. Why not?. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data . 523–534

    2009

  8. [16]

    Chad Creighton and Samir Hanash. 2003. Mining gene expression databases for association rules. Bioinformatics 19, 1 (2003), 79–86

    2003

  9. [17]

    Susan Davidson, Daniel Deutch, Nave Frost, Benny Kimelfeld, Omer Koren, and Mikaël Monet. 2022. ShapGraph: An Holistic View of Explanations through Provenance Graphs and Shapley Values. In Proceedings of the 2022 International Conference on Management of Data . 2373–2376

    2022

  10. [18]

    Davidson, Daniel Deutch, Nave Frost, Benny Kimelfeld, Omer Koren, and Mikaël Monet

    Susan B. Davidson, Daniel Deutch, Nave Frost, Benny Kimelfeld, Omer Koren, and Mikaël Monet. 2022. ShapGraph: An Holistic View of Explanations through Provenance Graphs and Shapley Values. InSIGMOD ’22: International Conference on Management of Data, Philadelphia, PA, USA, Jun...

    2022

  11. [19]

    Daniel Deutch, Nave Frost, Amir Gilad, and Oren Sheffer. 2021. Explanations for data repair through shapley values. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 362–371

    2021

  12. [20]

    Daniel Deutch, Amir Gilad, Tova Milo, and Amit Somech. 2020. ExplainED: explanations for EDA notebooks. Proceedings of the VLDB Endowment 13, 12 (2020), 2917–2920

    2020

  13. [21]

    Jose A Diaz-Garcia, M Dolores Ruiz, and Maria J Martin-Bautista. 2022. A survey on the use of association rules mining techniques in textual social media. Artificial Intelligence Review (2022), 1–26

    2022

  14. [22]

    Onur Dogan, Furkan Can Kem, and Basar Oztaysi. 2022. Fuzzy association rule mining approach to identify e-commerce product association considering sales amount. Complex & Intelligent Systems 8, 2 (2022), 1551–1560

    2022

  15. [23]

    Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of inter- pretable machine learning. arXiv preprint arXiv:1702.08608 (2017)

    2017 arXiv

  16. [24]

    Alex A Freitas. 1998. On objective measures of rule surprisingness. In European Symposium on Principles of Data Mining and Knowledge Discovery . Springer, 1–9

    1998

  17. [25]

    Liqiang Geng and Howard J Hamilton. 2006. Interestingness measures for data mining: A survey. ACM Computing Surveys (CSUR) 38, 3 (2006), 9–es

    2006

  18. [26]

    Green, G

    T.J. Green, G. Karvounarakis, and V. Tannen. 2007. Provenance semirings. In PODS. 31–40

    2007

  19. [27]

    Michael Hahsler and Radoslaw Karpienko. 2017. Visualizing association rules in hierarchical groups. Journal of Business Economics 87, 3 (2017), 317–335

    2017

  20. [28]

    Jiawei Han, Jian Pei, and Yiwen Yin. 2000. Mining frequent patterns without candidate generation. ACM sigmod record 29, 2 (2000), 1–12

    2000

  21. [29]

    Robert J Hilderman and Howard J Hamilton. 2013. Knowledge discovery and measures of interest. Vol. 638. Springer Science & Business Media

    2013

  22. [30]

    Hao Huang, Qian Yan, Wei Lu, Huaizhong Lin, Yunjun Gao, and Lei Chen. 2019. LERI: Local Exploration for Rare-Category Identification. IEEE Transactions on Knowledge and Data Engineering 32, 9 (2019), 1761–1772

    2019

  23. [31]

    Richard A Huebner. 2009. Diversity-based interestingness measures for associa- tion rule mining. Proceedings of ASBBS 16, 1 (2009)

    2009

  24. [32]

    Mark Ibrahim, Melissa Louie, Ceena Modarres, and John Paisley. 2019. Global explanations of neural networks: Mapping the landscape of predictions. In Pro- ceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society . 279–287

    2019

  25. [33]

    ISOLET Dataset (UCI). 2024. https://archive.ics.uci.edu/dataset/54/isolet. (2024)

    2024

  26. [34]

    Szymon Jaroszewicz and Dan A Simovici. 2002. Pruning redundant association rules using maximum entropy principle. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 135–147

    2002

  27. [35]

    Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, and Rajesh Ranganath. 2021. FastSHAP: Real-Time Shapley Value Estimation. InInternational Conference on Learning Representations

    2021

  28. [36]

    COVID-19 Symptoms (Kaggle). 2020. https://www.kaggle.com/datasets/ iamhungundji/covid19-symptoms-checker. (2020)

    2020

  29. [37]

    Spotify Dataset (Kaggle). 2024. https://www.kaggle.com/mrmorj/ dataset-of-songs-in-spotify. (2024)

    2024

  30. [38]

    Satya Katragadda, Raju Gottumukkala, Ravi Teja Bhupatiraju, Azmyin Md Kamal, Vijay Raghavan, Henry Chu, Ramesh Kolluru, and Ziad Ashkar. 2021. Association mining based approach to analyze COVID-19 response and case growth in the United States. Scientific Reports 11, 1 (2021), 1–12

    2021

  31. [39]

    Brian Lent, Arun Swami, and Jennifer Widom. 1997. Clustering association rules. In Proceedings 13th International Conference on Data Engineering . IEEE, 220–231

    1997

  32. [40]

    Zhi Li, Xuyu Li, Runhua Tang, and Lin Zhang. 2021. Apriori algorithm for the data mining of global cyberspace security issues for human participatory based on association rules. Frontiers in Psychology 11 (2021), 582480

    2021

  33. [41]

    Song Lin and Donald E Brown. 2006. An outlier-based data association method for linking criminal incidents. Decision Support Systems 41, 3 (2006), 604–615

    2006

  34. [42]

    Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. 2021. Ex- plainable ai: A review of machine learning interpretability methods. Entropy 23, 1 (2021), 18

    2021

  35. [43]

    Ester Livshits, Leopoldo Bertossi, Benny Kimelfeld, and Moshe Sebag. 2019. The Shapley value of tuples in query answering. arXiv preprint arXiv:1904.08679 (2019)

    2019 arXiv

  36. [44]

    Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence 2, 1 (2020), 56–67

    2020

  37. [45]

    Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 476...

    2017

  38. [46]

    Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30. 4765–4774

    2017

  39. [47]

    Yuyu Luo, Xuedi Qin, Nan Tang, and Guoliang Li. 2018. DeepEye: Towards Automatic Data Visualization. ICDE

    2018

  40. [48]

    Wes McKinney. 2010. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference , Stéfan van der Walt and Jarrod Millman (Eds.). 51 – 56

    2010

  41. [49]

    Rory Mitchell, Joshua Cooper, Eibe Frank, and Geoffrey Holmes. 2022. Sampling permutations for shapley value estimation. Journal of Machine Learning Research 23, 43 (2022), 1–46

    2022

  42. [50]

    United States Department of Transportation. 2015. 2015 Flight Delays and Cancellations. https://www.kaggle.com/usdot/flight-delays. (2015)

    2015

  43. [51]

    Judea Pearl et al. 2009. Causal inference in statistics: An overview. Statistics surveys 3 (2009), 96–146

    2009

  44. [52]

    SHARQ Github Repository. 2024. https://anonymous.4open.science/r/SHARQ- 0EC4. (2024)

    2024

  45. [53]

    Why should i trust you?

    Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why should i trust you?" Explaining the predictions of any classifier. InProceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining . 1135–1144

    2016

  46. [54]

    Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018. Anchors: High- precision model-agnostic explanations. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32

    2018

  47. [55]

    Avi Rosenfeld. 2021. Better metrics for evaluating explainable artificial intelli- gence. In Proceedings of the 20th international conference on autonomous agents and multiagent systems. 45–50

    2021

  48. [56]

    Mirka Saarela and Susanne Jauhiainen. 2021. Comparison of feature importance measures as explanations for classification models. SN Applied Sciences 3, 2 14 (2021), 272

    2021

  49. [57]

    Ahmed M Salih, Zahra Raisi-Estabragh, Ilaria Boscolo Galazzo, Petia Radeva, Steffen E Petersen, Karim Lekadir, and Gloria Menegaz. 2024. A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME. Advanced Intelligent Systems (2024), 2400304

    2024

  50. [58]

    LS SHAPLEY. 1953. A value for n-person games. Contributions to the Theory of Games 28 (1953), 307–317

    1953

  51. [59]

    L. S. Shapley. 1953. 17. A Value for n-Person Games . Princeton University Press, Princeton, 307–318. https://doi.org/doi:10.1515/9781400881970-018

    1953 doi

  52. [60]

    Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning important features through propagating activation differences. In International Conference on Machine Learning . PMLR, 3145–3153

    2017

  53. [61]

    Manish Singh, Michael J Cafarella, and HV Jagadish. 2016. DBExplorer: Ex- ploratory Search in Databases. EDBT (2016)

    2016

  54. [62]

    Srikant and R

    R. Srikant and R. Agrawal. 1997. Mining generalized association rules. Future Gener. Comput. Syst. 13, 2 (1997)

    1997

  55. [63]

    Arjun Srinivasan, Steven M Drucker, Alex Endert, and John Stasko. 2018. Aug- menting visualizations with interactive data facts to facilitate interpretation and communication. IEEE transactions on visualization and computer graphics 25, 1 (2018), 672–681

    2018

  56. [64]

    Erik Štrumbelj and Igor Kononenko. 2014. Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems 41, 3 (2014), 647–665

    2014

  57. [65]

    Grażyna Suchacka and Grzegorz Chodak. 2017. Using association rules to as- sess purchase probability in online stores. Information Systems and e-Business Management 15, 3 (2017), 751–780

    2017

  58. [66]

    Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In International Conference on Machine Learning . PMLR, 3319–3328

    2017

  59. [67]

    Pang-Ning Tan and Vipin Kumar. 2000. Interestingness measures for association patterns: A perspective. (2000)

    2000

  60. [68]

    Meera Tandan, Yogesh Acharya, Suresh Pokharel, and Mohan Timilsina. 2021. Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in biology and medicine 131 (2021), 104249

    2021

  61. [69]

    Bo Tang, Shi Han, Man Lung Yiu, Rui Ding, and Dongmei Zhang. 2017. Extracting top-k insights from multi-dimensional data. In Proceedings of the 2017 ACM International Conference on Management of Data . 1509–1524

    2017

  62. [70]

    Census Bureau

    U.S. Census Bureau . 2024. Annual Social and Economic Supplements. https: //www.census.gov/data/datasets/time-series/demo/cps/cps-asec.html. (2024)

    2024

  63. [71]

    Pak Chung Wong, Paul Whitney, and Jim Thomas. 1999. Visualizing associa- tion rules for text mining. In Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis’ 99). IEEE, 120–123

    1999

  64. [72]

    Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2016. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. TVCG (2016)

    2016

  65. [73]

    Eugene Wu and Samuel Madden. 2013. Scorpion: Explaining away outliers in aggregate queries. (2013)

    2013

  66. [74]

    Yuejin Zhang, Lingling Zhang, Guangli Nie, and Yong Shi. 2009. A survey of interestingness measures for association rules. In 2009 International Conference on Business Intelligence and Financial Engineering . IEEE, 460–463

    2009

  67. [75]

    Alexander Zien, Nicole Krämer, Sören Sonnenburg, and Gunnar Rätsch. 2009. The feature importance ranking measure. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II 20 . Spri...

    2009

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.