(EDSFCA): Efficient Document Subspace Clustering in High-Dimensional Data using Fast Clustering Algorithm - Radhika, KR; Pushpa C N, Pushpa C N; Thriveni, J; Venugopal, KR (International Journal of Computer Sciences and Engineering, 2019)
In the contemporary age of digitization, majority of the users are constantly moving on the prevalent computing in the area of telecommunication and social networking. The data may be produced from several resources from an individual to organization level. The existing data mining techniques are not suitable, due to the features of non structured and semistructuredness in data which leads to dimensionality problems. To overcome these problems, an Efficient Document Subspace Clustering in High Dimensional Data using Fast Clustering Algorithm (EDSFCA) is proposed. This method performs Datamining techniques like preprocessing and removing of corrupted and repetative data from the subspace clusters. The twitter data is taken as an input and is divided into clusters in order to provide a characteristic of high-dimensional data. This information is organized arbitrarily in subspace clusters and then segmentation is done on data points. The EDSFCA approach does the cluster analysis of datasets in smallest period of time.