Optimisation of outlier data mining algorithm for large datasets based on unit Online publication date: Tue, 04-Jul-2023
by Yizhi Li; Xiangming Zhou
International Journal of Information Technology and Management (IJITM), Vol. 22, No. 3/4, 2023
Abstract: This article aims to study the cell-based outlier data mining algorithm for large datasets, and to further improve the profit group data mining algorithm. This experiment first uses mathematical statistical analysis methods to study the optimisation of large data sets based on the unit-based outlier data mining algorithm and the proportion of data mining in various categories of the internet of things; then uses data statistics methods to classify and analyse large data sets, and test normal data mining optimisation algorithms. Finally, the experimental data shows that data mining has been significantly improved in terms of speed, intelligent internet of things, intelligent transportation, big data, genetic algorithms, etc. Experimental data testing shows that the algorithm can quickly and efficiently mine outliers in the dataset, and increase the detection speed of outliers by about 32%, which has guiding significance for outlier data mining in large datasets.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information Technology and Management (IJITM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook