Research on Association Rules Data Mining Based on Improved K-means Algorithm

Huang JILONG

doi:10.1109/AEECA55500.2022.9918875

Research on Association Rules Data Mining Based on Improved K-means Algorithm

Huang JILONG^*

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

1 Citation (Scopus)

Abstract

The process of data mining is essentially the process of finding value in huge and random data. With the development of the times, information technology has continuously entered various fields, which has greatly expanded the amount and types of data that can be used for mining. In data mining, association rules are the association relationships between data. By grasping the association rules between massive data, users can have a special understanding of the data to assist judgment. This paper improves the K-means algorithm based on the three selected test indicators, eliminates redundant rules through certain criteria, obtains the initial point through triangle iteration under the K-means rule, and clusters the rules. Through the research in this paper, it can be confirmed that the aggregation of similar rules can help data analysts quickly obtain useful rules and help the further advancement of the analysis process.

Original language	English
Title of host publication	2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022: Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	610-613
Number of pages	4
ISBN (Electronic)	9781665480901
DOIs	https://doi.org/10.1109/AEECA55500.2022.9918875
Publication status	Published - 2022
Externally published	Yes
Event	The 3rd IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA2022 - Dalian, China Duration: 20 Aug 2022 → 21 Aug 2022

Conference

Conference	The 3rd IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA2022
Abbreviated title	AEECA2022
Country/Territory	China
City	Dalian
Period	20/08/22 → 21/08/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Association rules
Clustering
Data mining
K-means

Access to Document

10.1109/AEECA55500.2022.9918875

Cite this

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title = "Research on Association Rules Data Mining Based on Improved K-means Algorithm",

abstract = "The process of data mining is essentially the process of finding value in huge and random data. With the development of the times, information technology has continuously entered various fields, which has greatly expanded the amount and types of data that can be used for mining. In data mining, association rules are the association relationships between data. By grasping the association rules between massive data, users can have a special understanding of the data to assist judgment. This paper improves the K-means algorithm based on the three selected test indicators, eliminates redundant rules through certain criteria, obtains the initial point through triangle iteration under the K-means rule, and clusters the rules. Through the research in this paper, it can be confirmed that the aggregation of similar rules can help data analysts quickly obtain useful rules and help the further advancement of the analysis process.",

keywords = "Association rules, Clustering, Data mining, K-means",

author = "Huang JILONG",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; The 3rd IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA2022, AEECA2022 ; Conference date: 20-08-2022 Through 21-08-2022",

year = "2022",

doi = "10.1109/AEECA55500.2022.9918875",

language = "English",

pages = "610--613",

booktitle = "2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022: Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

JILONG, H 2022, Research on Association Rules Data Mining Based on Improved K-means Algorithm. in 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022: Proceedings. Institute of Electrical and Electronics Engineers Inc., pp. 610-613, The 3rd IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA2022, Dalian, China, 20/08/22. https://doi.org/10.1109/AEECA55500.2022.9918875

Research on Association Rules Data Mining Based on Improved K-means Algorithm. / JILONG, Huang.
2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022: Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 610-613.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - Research on Association Rules Data Mining Based on Improved K-means Algorithm

AU - JILONG, Huang

PY - 2022

Y1 - 2022

N2 - The process of data mining is essentially the process of finding value in huge and random data. With the development of the times, information technology has continuously entered various fields, which has greatly expanded the amount and types of data that can be used for mining. In data mining, association rules are the association relationships between data. By grasping the association rules between massive data, users can have a special understanding of the data to assist judgment. This paper improves the K-means algorithm based on the three selected test indicators, eliminates redundant rules through certain criteria, obtains the initial point through triangle iteration under the K-means rule, and clusters the rules. Through the research in this paper, it can be confirmed that the aggregation of similar rules can help data analysts quickly obtain useful rules and help the further advancement of the analysis process.

AB - The process of data mining is essentially the process of finding value in huge and random data. With the development of the times, information technology has continuously entered various fields, which has greatly expanded the amount and types of data that can be used for mining. In data mining, association rules are the association relationships between data. By grasping the association rules between massive data, users can have a special understanding of the data to assist judgment. This paper improves the K-means algorithm based on the three selected test indicators, eliminates redundant rules through certain criteria, obtains the initial point through triangle iteration under the K-means rule, and clusters the rules. Through the research in this paper, it can be confirmed that the aggregation of similar rules can help data analysts quickly obtain useful rules and help the further advancement of the analysis process.

KW - Association rules

KW - Clustering

KW - Data mining

KW - K-means

UR - https://www.scopus.com/pages/publications/85141409866

U2 - 10.1109/AEECA55500.2022.9918875

DO - 10.1109/AEECA55500.2022.9918875

M3 - Conference paper (refereed)

AN - SCOPUS:85141409866

SP - 610

EP - 613

BT - 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022: Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - The 3rd IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA2022

Y2 - 20 August 2022 through 21 August 2022

ER -

Research on Association Rules Data Mining Based on Improved K-means Algorithm

Abstract

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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