An unsupervised grid-based approach for clustering analysis

In recent years, the growing volume of data in numerous clustering tasks has greatly boosted the existing clustering algorithms in dealing with very large datasets. The K-means has been one of the most popular clustering algorithms because of its simplicity and easiness in application, but its efficiency and effectiveness for large datasets are often unacceptable. In contrast to the K-means algorithm, most existing grid-clustering algorithms have linear time and space complexities and thus can perform well for large datasets. In this paper, we propose a grid-based partitional algorithm to overcome the drawbacks of the K-means clustering algorithm. This new algorithm is based on two major concepts: 1) maximizing the average density of a group of grids instead of minimizing the minimal square error which is applied in the K-means algorithm, and 2) using gridclustering algorithms to thoroughly reformulate the object-driven assigning in the K-means algorithm into a new grid-driven assigning. Consequently, our proposed algorithm obtains an average speed-up about 10–100 times faster and produces better partitions than those by the K-means algorithm. Also, compared with the K-means algorithm, our proposed algorithm has ability to partition any dataset when the number of clusters is unknown. The effectiveness of our proposed algorithm has been demonstrated through successfully clustering datasets with different features in comparison with the other three typical clustering algorithms besides the K-means algorithm.