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

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Authors

Balakrishnan Subramanian¹

1. Sri Krishna College of Engineering and Technology

Conference / event

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

Poster summary

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.

Keywords

Breast cancer, Clustering, Opinion mining

Research areas

Medicine, Computer and Information Science

References

No data provided

Funding

No data provided

Supplemental files

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Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

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

Creative Commons license

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.