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Knowledge mining in big data — A lesson from algebraic geometry | IEEE Conference Publication | IEEE Xplore

Knowledge mining in big data — A lesson from algebraic geometry


Abstract:

A granular computing (GrC) approach of a mathematical framework for “knowledge mining in Big Data” is illustrated by using some idea from algebraic geometry: (1) For exam...Show More

Abstract:

A granular computing (GrC) approach of a mathematical framework for “knowledge mining in Big Data” is illustrated by using some idea from algebraic geometry: (1) For example, the ring of the integers, denoted by Z, is a model U of `Big Data' (the discourse of universe of `Big Data'). (2) The selection of the set of prime ideals is an example of granulating (MAPping) the “Big Data” U into granular structure. (3) To compute the hidden geometric structure of Spec(Z) (e.g., Zariski topology) is to compute (to REDUCE) the quotient structure and and to interpret into knowledge structure. The transformation of algebraic structure of Z to geometric structure of Spec(Z) is the GrC framework of “knowledge mining in Big Data”.
Date of Conference: 13-15 December 2013
Date Added to IEEE Xplore: 17 February 2014
Electronic ISBN:978-1-4799-1282-7
Conference Location: Beijing, China

References

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