An improved algorithm to handle noise objects in the process of clustering

Autor:	Hasanthi A Pathberiya, Liwan L. Hansen, C. D. Tilakaratne
Rok vydání:	2019
Předmět:	Set (abstract data type) Noise Artificial neural network business.industry Computer science Process (computing) k-means clustering Unsupervised learning Pattern recognition Artificial intelligence Cluster analysis business Ward's method
Zdroj:	International Journal of Data Science. 4:1
ISSN:	2053-082X 2053-0811
Popis:	Cluster analysis is considered as an approach for unsupervised learning. It tends to recognise hidden grouping structure in a set of objects using a predefined set of rules. Objects occupying unusual characteristics add noise to the data space. As a result, complexities and misinterpretation in clustering structures will arise. This study aims at proposing a novel iterative approach to eradicate the effect of noise objects in the process of deriving clusters of data. Performance of the proposed approach is tested on partitioning, hierarchical and neural network based clustering algorithms using both simulated and standard datasets supplemented with noise. An improvement in the quality of clustering structure resulted from the proposed approach is witnessed, compared to that of conventional clustering algorithms.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b6f64205f32273f8eca236eba69e437 https://doi.org/10.1504/ijds.2019.098358 Zobrazit plný text záznamu