title,doi,url,abstract,journal,publication_year,pmid,arxiv
Survey on Clustering High-Dimensional data using Hubness, https://doi.org/10.32628/CSEIT195671, https://ijsrcseit.com/CSEIT195671, 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., International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2020, CSEIT195671