Creative machines are an old idea, but only recently computational creativity has established itself as a research field with its own identity and research agenda. The goal of computational creativity research is to model, simulate, or enhance creativity using computational methods. Data mining and machine learning can be used in a number of ways to help computers learn how to be creative, such as learning to generate new artifacts or to evaluate various qualities of newly generated artifacts. In this review paper, we give an overview of research in computational creativity with a focus on the roles that data mining and machine learning have had and could have in creative systems. INTRODUCTION While artificial intelligence has experienced remarkable advances in the last decades and has reached maturity as a research field, its sibling, computational creativity, is in earlier phases of its development. Computational creativity can be characterized in a manner parallel to artificial intelligence: Where artificial intelligence studies how to perform tasks which would be deemed intelligent if performed by a human, computational creativity studies performances which would be deemed creative if performed by a human.As a research field, the goal of computational creativity is to model or simulate creativity, or to enhance human creativity using computational methods. Such tasks could be in musical creativity, verbal creativity, visual creativity, creative problem solving, or some other area requiring creative skills.A major question in computational creativity is if and when computers can be credited with originality. Already Ada Lovelace, the 'first programmer', is quoted to believe that machines can create but not really originate anything. Obviously, a purely preprogrammed generative system can be criticized for only doing what it was told to do and thus for having little if any creativity. Some form of adaptivity or self-determinism seems necessary to attribute any creative autonomy or originality to a creative system. 1,2 This is where methods from data mining and machine learning can help.Here data mining and machine learning are broadly understood as methods that analyze data and make useful discoveries or inferences from them. Such methods can be used in creative systems, e.g., to learn how to recognize desirable qualities in produced artifacts, or even to produce artifacts, helping these systems produce novel and valuable results; however, there are obvious risks. For instance, automatic analysis of Western pop music 3 could reveal patterns that can be used to generate fairly good imitations of the given music. Imitation, however, is not really an original or creative act, and it is not the goal of computational creativity research. We will return to this in the next sections.The goal of this article is to introduce the research field of computational creativity to data miners and machine learners. We present computational creativity research from this viewpoint and structure the review by the different rol...

The ability to associate concepts is an important factor of creativity. We investigate the power of simple word co-occurrence analysis in tasks requiring verbal creativity. We first consider the Remote Associates Test, a psychometric measure of creativity. It turns out to be very easy for computers with access to statistics from a large corpus. Next, we address generation of poetry, an act with much more complex creative aspects. We outline methods that can produce surprisingly good poems based on existing linguistic corpora but otherwise minimal amounts of knowledge about language or poetry. The success of these simple methods suggests that corpus-based approaches can be powerful tools for computational support of creativity.

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Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between

Abstract-A fluent ability to associate tasks, concepts, ideas, knowledge and experiences in a relevant way is often considered an important factor of creativity, especially in problem solving. We are interested in providing computational support for discovering such creative associations.In this paper we design minimally supervised methods that can perform well in the remote associates test (RAT), a well-known psychometric measure of creativity. We show that with a large corpus of text and some relatively simple principles, this can be achieved. We then develop methods for a more general word association model that could be used in lexical creativity support systems, and which also could be a small step towards lexical creativity in computers.