By Johannes Fürnkranz
Rules – the clearest, so much explored and top understood kind of wisdom illustration – are rather vital for information mining, as they provide the simplest tradeoff among human and computer understandability. This e-book provides the basics of rule studying as investigated in classical laptop studying and smooth info mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule studying, hence bridging the space among attribute-value studying and inductive good judgment programming, and delivering entire insurance of most crucial parts of rule learning.
The publication can be utilized as a textbook for educating desktop studying, in addition to a complete connection with learn within the box of inductive rule studying. As such, it pursuits scholars, researchers and builders of rule studying algorithms, providing the elemental rule studying techniques in adequate breadth and intensity to permit the reader to appreciate, boost and practice rule studying suggestions to real-world data.
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Extra info for Foundations of Rule Learning
At the upper level, we have a multiclass classification problem which is transformed into a series of concept learning tasks. For each concept learning task there is a training set consisting of positive and negative examples of the target concept. 1 will be transformed into a set consisting of two positive examples (#4 and #9) and 16 negative examples (all others). Similar transformations are then made for the concepts sports (4 positive and 12 negative examples) and mini (12 positive and 6 negative examples).
An in-depth survey of association rule discovery is beyond the scope of this book, and, indeed, the subject has already been covered in other monographs (Adamo, 2000; Zhang & Zhang, 2002). , the level-wise search algorithm, which forms the basis of APRIORI and related techniques, is briefly explained in Sect. 2), but for a systematic treatment of the subject we refer the reader to the literature. 2 Subgroup Discovery In subgroup discovery the task is to find sufficiently large population subgroups that have a significantly different class distribution than the entire population (the entire dataset).
1 The Covering Algorithm The covering or separate-and-conquer strategy has its origins in the AQ family of algorithms (Michalski, 1969). The term separate-and-conquer has been coined by Pagallo and Haussler (1990) because of the way of developing a theory that characterizes this learning strategy: learn a rule that covers a part of the given training examples, remove the covered examples from the training set (the separate part), and recursively learn another rule that covers some of the remaining examples (the conquer part) until no examples remain.