Int. J. Data Analysis Techniques and Strategies, Vol. 10, No. 1, 2018 57 Copyright © 2018 Inderscience Enterprises Ltd. An efficient method for batch updates in OPTICS cluster ordering Dhruv Kumar, Poonam Goyal* and Navneet Goyal Department of Computer Science, Birla Institute of Technology and Science, Pilani, 333 031, India Email: f2010526@pilani.bits-pilani.ac.in Email: poonam@pilani.bits-pilani.ac.in Email: goel@pilani.bits-pilani.ac.in *Corresponding author Abstract: DBSCAN is one of the popular density-based clustering algorithms, but requires re-clustering the entire data when the input parameters are changed. OPTICS overcomes this limitation. In this paper, we propose a batch-wise incremental OPTICS algorithm which performs efficient insertion and deletion of a batch of points in a hierarchical cluster ordering, which is the output of OPTICS. Only a couple of algorithms are available in the literature on incremental versions of OPTICS. This can be attributed to the sequential access patterns of OPTICS. The existing incremental algorithms address the problem of incrementally updating the hierarchical cluster ordering for point-wise insertion/deletion, but these algorithms are only good for infrequent updates. The proposed incremental OPTICS algorithm performs batch-wise insertions/deletions and is suitable for frequent updates. It produces exactly the same hierarchical cluster ordering as that of classical OPTICS. Real datasets have been used for experimental evaluation of the proposed algorithm and results show remarkable performance improvement over the classical and other existing incremental OPTICS algorithms. Keywords: OPTICS; incremental clustering; batch updates; density-based clustering. Reference to this paper should be made as follows: Kumar, D., Goyal, P. and Goyal, N. (2018) ‘An efficient method for batch updates in OPTICS cluster ordering’, Int. J. Data Analysis Techniques and Strategies, Vol. 10, No. 1, pp.57–80. Biographical notes: Dhruv Kumar received his BE in Computer Science from Birla Institute of Technology and Science, Pilani India and is pursuing ME in Computer Science. He achieved first rank in BE in Computer Science. Poonam Goyal received his PhD degree in Mathematics from Indian Institute of Technology Roorkee and Master’s in Software Systems from Birla Institute of Technology and Science, Pilani, India. She is currently an Associate Professor in the Department of Computer Science, Birla Institute of Technology and Science, Pilani, India. She is associated with the Advanced Data Analytics and Parallel Technologies Lab and also with Web Intelligence and Social Computing Lab. Her research interests include data mining, high performance computing and information retrieval.