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Change mining in evolving fuzzy concept lattices

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Abstract

Fuzzy formal concept analysis enables us to add structure to data by ide ntifying coherent groups of related objects and attributes. In a situation where data is added dynamically, the concept lattice may evolve in different ways—either in content (more objects added to existing concepts) or in structure (entirely new concepts are created). This change can be monitored and quantified by means of a recently defined distance metric. In this paper, we present a new and more efficient algorithm for calculating the fuzzy distance between concepts, and hence between concept lattices. We discuss the interpretation of the distance measure, and illustrate the evolution of concept lattices by simple examples and by an application of the distance measure to lattices derived from UK road safety data.

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Notes

  1. There is significant research effort in systems that do not rely on all classes being known a priori (evolving systems). However, most textbooks on machine learning formulate the problem as finding a function from some subspace of the data vectors to a finite set of known outputs.

  2. The initial study and software implementation was carried out by V. Theurlacher, Univ. of Montpellier, during a project placement.

  3. data.gov.uk/dataset/road-accidents-safety-data.

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Martin, T.P. Change mining in evolving fuzzy concept lattices. Evolving Systems 5, 259–274 (2014). https://doi.org/10.1007/s12530-014-9109-x

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  • DOI: https://doi.org/10.1007/s12530-014-9109-x

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