Combined secure approach based on whale optimization to improve the data classification for data analytics

The data clustering technique plays a significant role for the process of analyzing the data in various fields such as, data mining, big data and image processing. As the health care domain needs various data pro-cessing to detecting and diagnosing the disease, in existing image and data mining helps to identify and diagnosis the disease specific to cancer as it need lot of attention for clustering those data with proper detection and accuracy. Apart from the categories of skin cancer types like breast cancer, blood cancer, skin cancer, etc., skin cancer is more complicated disease as it needs proper detection at the earlier stage and treatment. In this paper, we have proposed a combined approach of neural based K means approach and whale data classification-based skin cancer approach. In this approach, we have applied multi-layer (K-Means with whale optimization algorithm) data classification to detect the cluster region. Then whale approach with data classification helps to determine the mass density of user-based data cluster and also train the classified data to optimize along with the predominant features. Finally, this combined approach to classify the skin cancer with respect to segmentation, feature, optimization. This firefly optimization helps to reduce the detection error rate at the early stage along with data accuracy and sensitivity. Re-garding the security aspects, the optimization algorithm is secure against DoS and DDoS to ensure data privacy and confidentiality based on the data accuracy and detection time parameters discussed. Our proposed method will be evaluated with the existing methods like K-Means with genetic; K-means with firefly optimization methods. (c) 2021 Elsevier B.V. All rights reserved.