202,504 research outputs found
Interactive Constrained Association Rule Mining
We investigate ways to support interactive mining sessions, in the setting of
association rule mining. In such sessions, users specify conditions (queries)
on the associations to be generated. Our approach is a combination of the
integration of querying conditions inside the mining phase, and the incremental
querying of already generated associations. We present several concrete
algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second
International Conference on Knowledge Discovery and Data Mining (DaWaK 2000
Effect fusion using model-based clustering
In social and economic studies many of the collected variables are measured
on a nominal scale, often with a large number of categories. The definition of
categories is usually not unambiguous and different classification schemes
using either a finer or a coarser grid are possible. Categorisation has an
impact when such a variable is included as covariate in a regression model: a
too fine grid will result in imprecise estimates of the corresponding effects,
whereas with a too coarse grid important effects will be missed, resulting in
biased effect estimates and poor predictive performance.
To achieve automatic grouping of levels with essentially the same effect, we
adopt a Bayesian approach and specify the prior on the level effects as a
location mixture of spiky normal components. Fusion of level effects is induced
by a prior on the mixture weights which encourages empty components.
Model-based clustering of the effects during MCMC sampling allows to
simultaneously detect categories which have essentially the same effect size
and identify variables with no effect at all. The properties of this approach
are investigated in simulation studies. Finally, the method is applied to
analyse effects of high-dimensional categorical predictors on income in
Austria
Disambiguation strategies for data-oriented translation
The Data-Oriented Translation (DOT) model { originally proposed in (Poutsma, 1998, 2003) and based on Data-Oriented Parsing (DOP) (e.g. (Bod, Scha, & Sima'an, 2003)) { is best described as a hybrid model of
translation as it combines examples, linguistic information and a statistical translation model. Although theoretically interesting, it inherits the computational complexity associated with DOP. In this paper, we focus on
one computational challenge for this model: efficiently selecting the `best' translation to output. We present four different disambiguation strategies in terms of how they are implemented in our DOT system, along with experiments
which investigate how they compare in terms of accuracy and
efficiency
Interactive Data Exploration with Smart Drill-Down
We present {\em smart drill-down}, an operator for interactively exploring a
relational table to discover and summarize "interesting" groups of tuples. Each
group of tuples is described by a {\em rule}. For instance, the rule tells us that there are a thousand tuples with value in the
first column and in the second column (and any value in the third column).
Smart drill-down presents an analyst with a list of rules that together
describe interesting aspects of the table. The analyst can tailor the
definition of interesting, and can interactively apply smart drill-down on an
existing rule to explore that part of the table. We demonstrate that the
underlying optimization problems are {\sc NP-Hard}, and describe an algorithm
for finding the approximately optimal list of rules to display when the user
uses a smart drill-down, and a dynamic sampling scheme for efficiently
interacting with large tables. Finally, we perform experiments on real datasets
on our experimental prototype to demonstrate the usefulness of smart drill-down
and study the performance of our algorithms
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