  We introduce HiPaR, a novel pattern-aided regression method for tabular data
containing both categorical and numerical attributes. HiPaR mines hybrid rules
of the form $p \Rightarrow y = f(X)$ where $p$ is the characterization of a
data region and $f(X)$ is a linear regression model on a variable of interest
$y$. HiPaR relies on pattern mining techniques to identify regions of the data
where the target variable can be accurately explained via local linear models.
The novelty of the method lies in the combination of an enumerative approach to
explore the space of regions and efficient heuristics that guide the search.
Such a strategy provides more flexibility when selecting a small set of jointly
accurate and human-readable hybrid rules that explain the entire dataset. As
our experiments shows, HiPaR mines fewer rules than existing pattern-based
regression methods while still attaining state-of-the-art prediction
performance.
