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Research on Data Consistency Detection Method Based on Interactive Matching

Published: 28 September 2021 Publication History

Abstract

Data consistency detection is a research hotspot in the field of data mining. Traditional data mining algorithms assume that the data is static. The current data classification models can handle static data well, but most of the data has the characteristics of huge scale and diverse changes. The performance of the classification model will be greatly reduced with changing data, so how to effectively detect the changes of the data is a wideconcern in the academic fields. By using decision tree as the background, and If-then rule as the core content of the data set, this paper discusses the feasibility of using rule interactive matching to detect the consistency. Further, a data consistency detection method based on interactive matching (IM-CC detection method) is given, and then several common UCI data sets are combined to analyze the characteristics and performance of the algorithm. Experimental results show that IM-CC has good interpretability and operability, can concisely integrate decision-making awareness into the decision-making process. So it has a wide range of application prospects.

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cover image ACM Other conferences
DSIT 2021: 2021 4th International Conference on Data Science and Information Technology
July 2021
481 pages
ISBN:9781450390248
DOI:10.1145/3478905
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2021

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Author Tags

  1. Classification rules
  2. Data consistency
  3. Interactive match

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DSIT 2021

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Overall Acceptance Rate 114 of 277 submissions, 41%

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