2009Unpublished venueRequires access

The Research of Data Pretreatment in CRM Based on Rough Set

Yunfeng Liu, Ke Lin, Yongke Yuan

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Abstract

The data pretreatment to reduce the redundancy of data and eliminate the data imprecise is the bottleneck in data mining. This article uses the superiority of the Rough Set model to eliminate the data imprecise, reduce the redundancy as far as possible in the data pretreatment and reduces the data capacity by the deduction of the attributes and deduction of values. It has also laid the foundation for the following data mining algorithm to reduce the time of process. By the example proof, it is one better algorithm according to incomplete and redundancy data processing.

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What this paper is about

The data pretreatment to reduce the redundancy of data and eliminate the data imprecise is the bottleneck in data mining. This article uses the superiority of the Rough Set model to eliminate the data imprecise, reduce the redundancy as far as possible in the data pretreatment and reduces the data capacity by the deduction of the attributes and deduction of values. It has also laid the foundation for the following data mining algorithm to reduce the time of process. By the example proof, it is one better algorithm according to incomplete and redundancy data processing.

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Available abstract

The data pretreatment to reduce the redundancy of data and eliminate the data imprecise is the bottleneck in data mining. This article uses the superiority of the Rough Set model to eliminate the data imprecise, reduce the redundancy as far as possible in the data pretreatment and reduces the data capacity by the deduction of the attributes and deduction of values. It has also laid the foundation for the following data mining algorithm to reduce the time of process. By the example proof, it is one better algorithm according to incomplete and redundancy data processing.

Key concepts: Redundancy (engineering), Data mining, Rough set, Bottleneck, Computer science, Data redundancy, Data set, Data modeling

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