Manuscript Number : CSEIT195671
Survey on Clustering High-Dimensional data using Hubness
Authors(2) :-Miss. Archana Chaudahri, Mr. Nilesh Vani Most data of interest today in data-mining applications is complex and is usually represented by many different features. Such high-dimensional data is by its very nature often quite difficult to handle by conventional machine-learning algorithms. This is considered to be an aspect of the well known curse of dimensionality. Consequently, high-dimensional data needs to be processed with care, which is why the design of machine-learning algorithms needs to take these factors into account. Furthermore, it was observed that some of the arising high-dimensional properties could in fact be exploited in improving overall algorithm design. One such phenomenon, related to nearest-neighbor learning methods, is known as hubness and refers to the emergence of very influential nodes (hubs) in k-nearest neighbor graphs. A crisp weighted voting scheme for the k-nearest neighbor classifier has recently been proposed which exploits this notion.
Miss. Archana Chaudahri Hubness, Clustering Methods, Datamining Techniques Publication Details Published in : Volume 6 | Issue 1 | January-February 2020 Article Preview
ME Scholar, Computer Engineering, GF's GCOE, Jalgaon, Jalgoan, Maharashtra, India
Mr. Nilesh Vani
Assistant Professor, Computer Engineering, GF's GCOE, Jalgon, Jalgoan, Maharashtra, India
Date of Publication : 2020-01-05
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 01-07
Manuscript Number : CSEIT195671
Publisher : Technoscience Academy
Journal URL : https://res.ijsrcseit.com/CSEIT195671
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