An effective and efficient grid-based data clustering algorithm using intuitive neighbor relationship for data mining - Cheng-Fa Tsai; Sheng-Chiang Huang (2015 International Conference on Machine Learning and Cybernetics (ICMLC), 2015)
This paper presents a new data clustering technique. It is a new grid-based clustering scheme by intuitive neighbor relationship for enhancing data clustering performance. Compared to other algorithms, this improved grid-based clustering algorithm substantially decreases repetitive clustering checks of neighboring grids and greatly improve the efficiency of data processing. Our simulations demonstrate that the proposed data clustering technique delivers better performance, in terms of clustering correctness rate and noise filtering rate, than perform other well-known existing algorithms, GOD-CS, CLIQUE and TING. To our best knowledge, the proposed data clustering technique may be the rapid method in the world currently.