Finding representative association rules from large rule collections

Davis, Warren L.; Schwarz, Peter; Terzi, Evimaria

doi:10.1137/1.9781611972795.45

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…(Sarma et al 2010) have investigated the view selection problem: given a materialized view V , a database D, and a collection S of sets of rules on D, find a set of rules in S that is as "close" to the view V and as compact as possible. In data mining, (Agrawal, Rantzau, and Terzi 2006;Davis, Schwarz, and Terzi 2009) investigated the problems of selecting contextual preference rules and association rules; these problems can be cast as variants of the rule selection problem considered here.…”

Section: Yes/nomentioning

confidence: 99%

Knowledge Refinement via Rule Selection

Kolaitis

Popa

Qian

2019

AAAI

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In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size.

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Section: Yes/nomentioning

confidence: 99%

Knowledge Refinement via Rule Selection

Kolaitis

Popa

Qian

2019

AAAI

View full text Add to dashboard Cite

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“…MaxEnt models have drawn much attention recently in the pattern mining community, especially on the topic of mining representative/succinct/surprising patterns, e.g. [25], as well as explicit summarization [9,34]. Wang and Parthasarathy [47] summarized a collection of frequent patterns by using a row-based MaxEnt model, heuristically mining and adding the most important itemsets in a levelwise fashion.…”

Section: Related Workmentioning

confidence: 99%