Laura Langohr
ABSTRACT
We propose a relatively simple yet powerful model for choosing relevant and non-redundant pieces of information. The model addresses data mining or information retrieval settings where relevance is measured with respect to a set of key or query objects, either specified by the user or obtained by a data mining step. The problem addressed is not only to identify other relevant objects, but also ensure that they are not related to possible negative query objects, and that they are not redundant with respect to each other.
The model proposed here only assumes a similarity or distance function for the objects. It has simple parameterization to allow for different behaviors with respect to query objects. We analyze the model and give two efficient, approximate methods. We illustrate and evaluate the proposed model on different applications: linguistics and social networks. The results indicate that the model and methods are useful in finding a relevant and non-redundant set of results.
While this area has been a popular topic of research, our contribution is to provide a simple, generic model that covers several related approaches while providing a systematic model for taking account of positive and negative query objects as well as non-redundancy of the output.
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Index Terms
- A model for mining relevant and non-redundant information
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