Titlebook: Bisociative Knowledge Discovery; An Introduction to C Michael R. Berthold Book‘‘‘‘‘‘‘‘ 2012 The Editor(s) (if applicable) and the Author(s)

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Towards Creative Information Exploration Based on Koestler’s Concept of Bisociationer new, surprising and valuable relationships in data that would not be revealed by conventional information retrieval, data mining and data analysis technologies. While our approach is inspired by work in the field of computational creativity, we are particularly interested in a model of creativity

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From Information Networks to Bisociative Information Networks where attempts are made to find unexpected relations across seemingly unrelated domains. Information networks, due to their flexible data structure, lend themselves perfectly to the integration of these heterogeneous data sources. This chapter provides an overview of different types of information

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Selecting the Links in BisoNets Generated from Document Collections In this chapter, we consider a methodology to find such bisociations using a BisoNet as a representation of knowledge. In a first step, we consider how to create BisoNets from several tex- tual databases taken from different domains using simple text-mining techniques. To achieve this, we introduce

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Cover Similarity Based Item Set Miningse of transactions. We, instead, strive to find item sets for which the similarity of the covers of the items (that is, the sets of transactions containing the items) exceeds a user-defined threshold. This approach yields a much better assessment of the association strength of the items, because it

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Patterns and Logic for Reasoning with Networksies expressing uncertainty. While . is based on graphs, ProbLog’s core language is that of the logic programming language Prolog. This chapter provides an overview of important concepts, terminology, and reasoning tasks addressed in the two systems. It does so in an informal way, focusing on intuiti

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Network Analysis: Overviewof representing data in numerous fields. Additionally, such networks can be created or derived from other types of information using, e.g., the methods given in Part II of this volume..This part of the book describes various network algorithms for the exploration and analysis of BisoNets. Their gene

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