Survey on Hubness - Based Clustering Algorithms

Clustering of high dimensionality data which can be seen in almost all fields these days is becoming very tedious process. The key disadvantage of high dimensional data which we can pen down is curse of dimensionality. As the magnitude of datasets grows the data points become sparse and density of area becomes less making it difficult to cluster that data which further reduces the performance of traditional algorithms used for clustering. To rout these toils hubness based algorithms were introduced as a variation to the these algorithms which influences the distribution of the data points among the k-nearest neighbor. The hubness is an unguided method which finds out which points appear more frequently in the k-nearest neighbor than other points in the dataset. This paper discuss the ways of clustering algorithms using hubness phenomenon. One of the methods is based on condensed nearest neighbor which is performed iteratively on the order independent data. The next algorithm is hinged for fuzzy based approaches which performs better on uncertain data ie. partially exposed or incomplete data. The proposed algorithms are basically used for increasing the efficiency and increasing predicting accuracy of the system.