A Study of Dimensionality Reduction Using Roughset Based K-Means

Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Feature reduction of pattern dimensionality using feature extraction and feature selection belongs to the data mining. To enhance the robustness of the k-means clustering algorithm and for visualization purpose the dimension reduction techniques may be employed. Randomized dimensionality reduction is the transformation of high-dimensional data into a significant illustration of reduced dimensionality that corresponds to the fundamental dimensionality of the data. K-means clustering algorithm often not well for high dimension datasets and error dimensionality reduction, hence, to improve the efficiency, the proposed system apply roughset theory based k-means on original data set and obtain a reduced dataset containing possibly uncorrelated variables.

Find By Topic