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 processing 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. Regarding 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.

数据聚类技术在数据挖掘、大数据和图像处理等各个领域的数据分析过程中发挥着重要作用。由于医疗保健领域需要各种数据处理来检测和诊断疾病，在现有的图像和数据挖掘中，有助于识别和诊断特定于癌症的疾病，因为需要大量关注以适当的检测和准确性对这些数据进行聚类。除了乳腺癌、血癌、皮肤癌等皮肤癌类型外，皮肤癌是一种更为复杂的疾病，需要在早期进行适当的发现和治疗。在本文中，我们提出了一种基于神经的 K 均值方法和基于鲸鱼数据分类的皮肤癌方法的组合方法。在这种方法中，我们应用了多层（K-Means 和鲸鱼优化算法）数据分类来检测聚类区域。然后使用数据分类的鲸鱼方法有助于确定基于用户的数据集群的质量密度，并训练分类数据以优化优势特征。最后，这种组合方法在分割、特征、优化方面对皮肤癌进行分类。这种萤火虫优化有助于降低早期检测错误率以及数据准确性和灵敏度。在安全方面，优化算法针对 DoS 和 DDoS 是安全的，以根据所讨论的数据准确性和检测时间参数确保数据隐私和机密性。我们提出的方法将使用现有的方法进行评估，例如 K-Means 与遗传；