327 research outputs found
A hybrid heuristic approach for attribute-oriented mining
We present a hybrid heuristic algorithm, clusterAOI, that generates a more interesting generalised table than obtained via attribute-oriented induction (AOI). AOI tends to overgeneralise as it uses a fixed global static threshold to cluster and generalise attributes irrespective of their features, and does not evaluate intermediate interestingness. In contrast, clusterAOI uses attribute features to dynamically recalculate new attribute thresholds and applies heuristics to evaluate cluster quality and intermediate interestingness. Experimental results show improved interestingness, better output pattern distribution and expressiveness, and improved runtime. © 2013 Elsevier B.V
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
Association Pattern Discovery of Import Export Items in Ethiopia
This paper examines the application of data mining to detect association pattern of customs administration data with market price and currency rate exchange in Ethiopia. The association rule method of data mining is used in this paper to generate the interesting pattern from the data. This study was done to identify the relationships between attributes of custom data and market price to clearly understand the nature of import-export items in Ethiopia. The results of the experiments carried out using association rules revealed that the technique of data mining is applicable to generate knowledge from import and export items in custom administration. Algorithms such as Apriori, Tertius, PredictiveApriori and FliteredApriori were used to generate the associations. One of the resulting associations indicates that there is a strong link between market price and textiles imported. The implication of this research finding is that it clearly identified the association of import-export items with the market price and the effects of those items on the market price and currency rate in Ethiopia
- …