TY  - JOUR
TI  - Survey on Clustering High-Dimensional data using Hubness
AU  - Miss. Archana Chaudahri
AU  -  Mr. Nilesh Vani
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2020/01/05
PY  - 2020
DO  - https://doi.org/10.32628/CSEIT195671
UR  - https://ijsrcseit.com/CSEIT195671
VL  - 6
IS  - 1
SP  - 01	
EP  - 07
AB  - 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.