Opinion mining for breast cancer disease using a priori and K-modes clustering algorithm

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作者

Balakrishnan Subramanian¹

1. Sri Krishna College of Engineering and Technology

会议/活动

FICR International Conference on Rising Threats in Expert Applications and Solutions (FICR-TEAS) 2022, January 2022 (虚拟会议)

海报摘要

Data mining procedures have been broadly used to mine learned data from medicinal information bases. Sentiment Mining is a procedure of programmed extraction of learning by method for conclusion of others about some specific item, theme or issue. Sentiment analysis implies decide the subjectivity, extremity (positive/negative) and extremity quality and so forth, with a bit of text. A priori hybrid algorithm and K-Modes Algorithm is used to cluster the opinions effectively and to improve the performance in online data set. Apriori-Hybrid, is the mix of count Apriori and Apriori-TID, which can mastermind the huge item sets and can improve the accuracy of collection of dangerous development and it can moreover uncover understanding into the basic part that enable each malady type to suffer and thrive, which in this way help in early revelation of the sort of threatening development. We propose Apriori-Hybrid as an extemporized calculation for tumor characterization.

关键词

Breast cancer, Clustering, Opinion mining

研究领域

Medicine, Computer and Information Science

参考文献

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基金

暂无数据

补充材料

暂无数据

附加信息

利益冲突

No competing interests were disclosed.

数据可用性声明

Data sharing not applicable to this poster as no datasets were generated or analyzed during the current study.

知识共享许可协议

Copyright © 2023 Subramanian. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.