Clustering algorithm regards robustness, stability, accuracy as performance measurement. In most recent studies, there has been no detailed investigation of resource usage such as CPU, memory, and executing time in most recent studies, one unanticipated finding was that several consensus functions waste many resources to give the outcomes of a small features dataset. In order to resolve these issues, we suggest two new measurement aspects that termed as resource usage and rapidity. Thus, we proposed a new method characterized by rapid implementation, as well as improved the accuracy with the best pure result. This model termed as Rapid Clustering with Semi-supervised Ensemble Density Centers. The strengths of our model is based on fewer iterations and depend on formulas and built-in functions instead of complex coding procedures, then pick objects with its features as cluster centers, and finally, utilize the semi-supervised learning specifically the pairwise constraints, and take advantage of density calculation. Furthermore, the experimental results demonstrated that our model gained the results rapidly with best accuracy and purity.

Advanced wireless communication is important in distribution systems for sharing information among Internet of Things (IoT) edges. Artificial intelligence (AI) analyzed the generated IoT data to make these decisions, ensuring efficient and effective operations. These technologies face significant security challenges, such as eavesdropping and adversarial attacks. Recent studies addressed this issue by using clustering analysis (CA) to uncover hidden patterns to provide AI models with clear interpretations. The high volume of overlapped samples in IoT data affects partitioning, interpretation, and reliability of CAs. Recent CA models have integrated machine learning techniques to address these issues, but struggle in the limited resources of IoT environments. These challenges are addressed by proposing a novel unsupervised lightweight distance clustering (DC) model based on data separation (). raises the tension between samples using cannot‐link relations to separate the overlap, thus DC provides the interpretations. The optimal time and space complexity enables DC‐ to be implemented on on‐edge computing, reducing data transmission overhead, and improving the robustness of the AI‐IoT application. Extensive experiments were conducted across various datasets under different circumstances. The results show that the data separated by improved the efficiency of the proposed solution, with DC outperforming the baseline model.

The robust unsupervised framework of clustering models is essential in numerous machine learning tasks due to its ability to identify hidden relations between samples, resulting in a comprehensive understanding and interpretation. Many datasets contain samples that naturally cannot link to each other, but the degree of similarity between them makes it almost impossible for clustering models to distinguish the differences; this is called the overlapping issue. However, integrating support vector machines, feature selection, and dimensional reduction techniques with clustering models might still be incapable of providing an optimal solution. As a result, it adversely affects performance and leads to inconsistent partitioning, unreasonable interpretation, and false confidence. This study addresses these issues by proposing a novel unsupervised data separation equation that is based on the concepts of tension and separation gained by finding the cannot-link relations based on cluster centroids. The equation validated in diverse scenarios to demonstrate its ability to improve outcomes. The experimental results prove that the proposed equation assists in reducing the reliance on parameter tuning and constraints, thereby enhancing performance and effectively addressing the challenge of outliers.

This work highlights issues that were not deeply investigated in previous studies on clustering solutions, which have essential impacts on performance in long-term real-world applications that are challenging to detect instantly. Thus, we addressed these issues by proposing two novel techniques: first, we expand the idea of clustering based on centroids to multiple sub-centroids that assist assignment functions in finding the optimal solution. In contrast to recent studies, we extended the concept of gravitational force toward clustering solutions. Finally, the introduced gap generation concept has been associated with these techniques to support a superior clustering solution. Our model is termed semi-supervised gravity clustering (SSGC). To demonstrate the strength of SSGC, we consider multiple performance measurements besides the traditional ones to validate the clustering models in various scenarios. The experimental results show that SSGC outperforms baseline models and successfully obtains the best performance of 30 different domain datasets. Finally, our methodology code is already released.

API integration burdens every consumer with provider-specific endpoints, schemas, versions, credentials, and error conventions, reworked at every provider change. We present SA2A (System Agent-to-Agent), an architecture in which one System Agent at each system's boundary accepts signed, intent-level envelopes, discovers capabilities dynamically, enforces deterministic local policy, and returns signed, field-filtered responses. Its defining property is mutual zero-trust: because any peer agent may be compromised, responses pass an entry gate at the requester exactly as requests do at the provider. A three-agent prototype (473 automated tests) was evaluated against a standard-toolkit REST baseline. All eighteen designed attacks failed, including six a request-only gate would admit: forged, replayed, over-broad, container-wrapped, and instruction-carrying responses. Adding a capability cost zero client-side steps against six for REST; under a breaking change an unmodified REST client broke on all five operations, one silently, while the SA2A client failed loudly and recovered without code edits. The price is +15.6 ms per call and +624 bytes per message; decomposition attributes 84.6% to durable audit and nonce writes, 1.6% to cryptography, and 0.1% to policy evaluation. Two ablations, over six provider layers and six response-gate checks, find one provider layer redundant and every gate check load-bearing.