45,242 research outputs found
Evaluation and optimization of frequent association rule based classification
Deriving useful and interesting rules from a data mining system is an essential and important task. Problems
such as the discovery of random and coincidental patterns or patterns with no significant values, and the
generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness
of rules generated by data mining algorithms are actively and constantly being examined and developed. In this
paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms,
combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task
Controlling False Positives in Association Rule Mining
Association rule mining is an important problem in the data mining area. It
enumerates and tests a large number of rules on a dataset and outputs rules
that satisfy user-specified constraints. Due to the large number of rules being
tested, rules that do not represent real systematic effect in the data can
satisfy the given constraints purely by random chance. Hence association rule
mining often suffers from a high risk of false positive errors. There is a lack
of comprehensive study on controlling false positives in association rule
mining. In this paper, we adopt three multiple testing correction
approaches---the direct adjustment approach, the permutation-based approach and
the holdout approach---to control false positives in association rule mining,
and conduct extensive experiments to study their performance. Our results show
that (1) Numerous spurious rules are generated if no correction is made. (2)
The three approaches can control false positives effectively. Among the three
approaches, the permutation-based approach has the highest power of detecting
real association rules, but it is very computationally expensive. We employ
several techniques to reduce its cost effectively.Comment: VLDB201
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules
The need to prediscretize numeric attributes before they can be used in
association rule learning is a source of inefficiencies in the resulting
classifier. This paper describes several new rule tuning steps aiming to
recover information lost in the discretization of numeric (quantitative)
attributes, and a new rule pruning strategy, which further reduces the size of
the classification models. We demonstrate the effectiveness of the proposed
methods on postoptimization of models generated by three state-of-the-art
association rule classification algorithms: Classification based on
Associations (Liu, 1998), Interpretable Decision Sets (Lakkaraju et al, 2016),
and Scalable Bayesian Rule Lists (Yang, 2017). Benchmarks on 22 datasets from
the UCI repository show that the postoptimized models are consistently smaller
-- typically by about 50% -- and have better classification performance on most
datasets
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