State-of-the-art rule mining algorithms rely on summarizing the training set into efficient data structures which allow to quickly answer arbitrary conjunctive queries about the data. The key limitation of such techniques is their memory consumption. Pre-post code trees (PPC-trees) which are the basis of several efficient association and classification rule mining algorithms, are only constructed as an intermediate representation and subsequently converted into a much more efficient N-lists structure. In this paper, we introduce partial pre-post code trees (P3C-trees), which are based on the idea that partial trees are iteratively constructed, and immediately converted into N-lists. This tight integration of these phases allows to avoid the memory bottleneck of a full PPC-tree construction, and thus enables these algorithms to tackle the memory scalability problem posed by large-scale datasets. Our experiments with big datasets confirm that the memory used by P3C-tree is orders of magnitude smaller than the memory consumed by PPC-tree, and the generated N-lists are also more effective than alternative structures such as Tidset or Diffset. Moreover, the N-list construction can also be considerably sped up with the P3C-tree structure.

Abstract The growing number of applications of machine learning and data mining in many domains—from agriculture to business, education, industrial manufacturing, and medicine—gave rise to new requirements for how to inspect and control the learned models. The research domain of explainable artificial intelligence (XAI) has been newly established with a strong focus on methods being applied post-hoc on black-box models. As an alternative, the use of interpretable machine learning methods has been considered—where the learned models are white-box ones. Black-box models can be characterized as representing implicit knowledge—typically resulting from statistical and neural approaches of machine learning, while white-box models are explicit representations of knowledge—typically resulting from rule-learning approaches. In this introduction to the special issue on ‘Explainable and Interpretable Machine Learning and Data Mining’ we propose to bring together both perspectives, pointing out commonalities and discussing possibilities to integrate them.

You hold in your hands the first issue of Data Mining and Knowledge Discovery in 10 years that does not bear the name of Geoffrey Webb as its editor-in-chief on the cover.It is a great honor but a much greater challenge for me to try to fill his shoes.And those are really big shoes to fill.As the editor-in-chief of this journal, Geoff has brought an enormous commitment and dedication to his role.His primary concern has always been the authors of the submitted papers, who should receive fast feedback of the highest quality, and, in case of acceptance, support for making the best out of their work.Most importantly, they should always be treated with the highest respect.As an editorial board member and action editor for this journal, I knew exactly that no review and no decision letter that I write will be unread by Geoff, and more than once he got back to me and pointed out shortcomings and improvements in my work for the journal.It is a daunting task to continue at this level, but I will do my best to live up to it.Thanks to his efforts, the journal is now in a healthy state.When Geoff took over in 2005, the journal published six issues in two volumes with a total of 22 papers on a bit more than 600 pages.In 2014, the journal has returned to publishing a single volume per year, but this volume consisted of four regular issues and a double special issue with a total of 48 papers on more than 1,600 pages.From 2005 onwards, the impact factor of the journal had continuously risen to its highest level of 2.95 in 2009.Since then, it has taken some ups and downs but essentially maintained its high level, which is the highest for journals that focus specifically on data mining.I am glad that Geoff will continue to serve on the Advisory Board of the journal.Where will the journal be heading in the next 10 years?One of the big challenges is open access publishing.

The task in multilabel classification is to predict for a given set of labels whether each individual label should be attached to an instance or not. Graded multilabel classification generalizes this setting by allowing to specify for each label a degree of membership on an ordinal scale. This setting can be frequently found in practice, for example when movies or books are assessed on a one-to-five star rating in multiple categories. In this paper, we propose to reformulate the problem in terms of preferences between the labels and their scales, which can then be tackled by learning from pair wise comparisons. We present three different approaches which make use of this decomposition and show on three datasets that we are able to outperform baseline approaches. In particular, we show that our solution, which is able to model pair wise preferences across multiple scales, outperforms a straight-forward approach which considers the problem as a set of independent ordinal regression tasks.

Overthelastdecade,localpatterndiscoveryhasbecomearapidlygrowingfield(Moriketal.2005),andarangeoftechniquesisavailableforproducingextensivecollectionsofpatterns.Becauseoftheexhaustivenatureofmostsuchtechniques,thepatterncol-lections provide a fairly complete picture of the information content of the database.However,suchso-calledlocalpatternsrepresentfragmentedknowledge,anditisoftennot clear how the pieces of the puzzle can be combined into a global model, which isoften the desirable result of a data mining process. Thus, the question of how to turnlarge collections of patterns into global models deserves attention.This special issue of the Data Mining and Knowledge Discovery Journal featuresa number of papers that represent the state of the art in building global models fromlocal patterns. In our view, a common ground of all the local pattern mining tech-niquesisthattheycanbeconsidered tobefeatureconstructiontechniques thatfollowdifferent objectives (or constraints). We will see that the redundancy of these patternsandtheselectionofsuitablesubsetsofpatternsareaddressedinseparatesteps,sothateach resulting feature is highly informative in the context of the global data miningproblem.In earlier work (Knobbe et al. 2008), a framework was proposed that provides ageneral outline of the activities involved. The framework, called From Local Patterns

Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. In this paper, we evaluate the spectrum of different search strategies to see whether separate-and-conquer rule learning algorithms are able to gain performance in terms of predictive accuracy or theory size by using more powerful search strategies like beam search or exhaustive search. Unlike previous results that demonstrated that rule learning algorithms suffer from over-searching, our work pays particular attention to the interaction between the search heuristic and the search strategy. Our results show that exhaustive search has primarily the effect of finding longer, but nevertheless more general rules than hill-climbing search. Thus, in cases where hillclimbing finds too specific rules, exhaustive search may help, while in others it may lead to over-generalization. 1

The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. To that end, we let a rule learner learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance of a rule from its training set characteristics. We investigate several variations of this basic scenario, including the question whether it is better to predict the performance of the candidate rule itself or of the resulting final rule. Our experiments on a number of independent evaluation sets show that the learned heuristics outperform standard rule learning heuristics. We also analyze their behavior in coverage space.