Data clustering is inevitable for crucial data analytic based applications. Though data clustering algorithms are capacious in the literature, there is always a room for efficient data clustering algorithms. This is due to the uncontrollable growth of data and its utilization. The data clustering may consider any of the data formats such as text, images, audio, video and so on. Due to the increasing utilization trend of digital images, this work intends to present a data clustering algorithm for digital images, which is based colour distance and Improvised DBSCAN (IDBSCAN) algorithm. The proposed IDBSCAN completely weeds out the annoying process of setting the initial parameters such as 𝜺 and 𝒎𝒊𝒏𝒑𝒕𝒔 by setting them automatically. The performance of the proposed work is analysed in terms of clustering accuracy, precision, recall, Fmeasure and time consumption rates. The proposed work outperforms the existing approaches with reasonable time consumption.
Joint non-negative and fuzzy coding with graph regularization for efficient data clustering
