Multimedia Tools and Applications https://doi.org/10.1007/s11042-023-17134-7 An incremental clustering method based on multiple objectives for dynamic data analysis Rajesh Dwivedi, Aruna Tiwari, Neha Bharill, Milind Ratnaparkhe, Rishabh Soni, Rahul Mahbubani, et al. [full author details at the end of the article] Received: 16 April 2023 / Revised: 6 August 2023 / Accepted: 15 September 2023 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023 Abstract Due to the advancement in big data and bioinformatics, the quantity and quality of raw data have exploded during the past two decades. Multiple sources contributed to the generation of very complex, diverse, and vast raw data. The generated data may conceal crucial patterns that need to be identified for data analysis. In the past few decades, a variety of clustering methods have been developed and have proven useful for data analysis. However, these methods are inappropriate for dynamic applications and only function with static data. To address this issue, we present a multi-objective incremental clustering method for processing dynamic data that generates and updates clusters in real-time. To improve the dynamic clustering pro- cess, the proposed method employs Euclidean distance to calculate the similarity between data points and constructs a fitness function with three primary clustering objective func- tions: inter-cluster distance, intra-cluster distance, and cluster density. The proposed method employs the concept of objective weighting, which allocates a weight to each objective in order to generate a single Pareto-optimal solution for the constructed fitness function. The proposed method outperforms other state-of-the-art methods on five benchmarks and three real-life plant genomics data sets. Keywords Multi-objective optimization · Incremental clustering · Intra-cluster distance · Inter-cluster distance · Cluster density 1 Introduction Nowadays, a lot of dynamic data is being generated; for example, every day, almost a billion people conduct search on Google. It is estimated that daily email traffic is around 300 billion. Every single day, people around the world compose about 230,000,000 tweets [10]. More than 30 petabytes (1015 bytes) of data created by Facebook users are stored, accessed, and analyzed by the social media platform. Aside from this, in bioinformatics, everyday, B Rajesh Dwivedi rajeshdwivedi@iiti.ac.in Extended author information available on the last page of the article 123 Content courtesy of Springer Nature, terms of use apply. Rights reserved.