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ABSTRACT

The primary aim of most data mining algorithms is to facilitate the discovery of concise and interpretable information from large amounts of data. However, many of the current formalizations of data mining algorithms have not quite reached this goal. One of the reasons for this is that the focus on using purely automated techniques has imposed several constraints on data mining algorithms. For example, any data mining problem such as clustering or association rules requires the specification of particular problem formulations, objective functions, and parameters. Such systems fail to take the user's needs into account very effectively. This makes it necessary to keep the user in the loop in a way which is both efficient and interpretable. One unique way of achieving this is by leveraging human visual perceptions on intermediate data mining results. Such a system combines the computational power of a computer and the intuitive abilities of a human to provide solutions which cannot be achieved by either. This paper will discuss a number of recent approaches to several data mining algorithms along these lines.

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