Density peak clustering (DPC) has garnered growing interest over recent decades due to its capability to identify clusters with diverse shapes and its resilience to the presence of noisy data. Most DPC-based methods exhibit high computational complexity. One approach to mitigate this issue involves utilizing density subgraphs. Nevertheless, the utilization of density subgraphs may impose restrictions on cluster sizes and potentially lead to an excessive number of small clusters. Furthermore, effectively handling these small clusters, whether through merging or separation, to derive accurate results poses a significant challenge, particularly in scenarios where the number of clusters is unknown. To address these challenges, we propose an adaptive density subgraph clustering algorithm (ADSC). ADSC follows a systematic three-step procedure. First, the highdensity regions in the dataset are recognized as density subgraphs based on k-nearest neighbor (KNN) density. Second, the initial clustering is carried out by utilizing an automated mechanism to identify the important density subgraphs and allocate outliers. Last, the obtained initial clustering results are further refined in an adaptive manner using the cluster self-ensemble technique, ultimately yielding the final clustering outcomes. The clustering performance of the proposed ADSC algorithm is evaluated on nineteen benchmark datasets. The experimental results demonstrate that ADSC possesses the ability to automatically determine the optimal number of clusters from intricate density data, all while maintaining high clustering efficiency. Comparative analysis against other well-known density clustering algorithms that require prior knowledge of cluster numbers reveals that ADSC consistently achieves comparable or superior clustering results.

The finite inverted beta mixture model (IBMM) has been proven to be efficient in modeling positive vectors. Under the traditional variational inference framework, the critical challenge in Bayesian estimation of the IBMM is that the computational cost of performing inference with large datasets is prohibitively expensive, which often limits the use of Bayesian approaches to small datasets. An efficient alternative provided by the recently proposed stochastic variational inference (SVI) framework allows for efficient inference on large datasets. Nevertheless, when using the SVI framework to address the non-Gaussian statistical models, the evidence lower bound (ELBO) cannot be explicitly calculated due to the intractable moment computation. Therefore, the algorithm under the SVI framework cannot directly use stochastic optimization to optimize the ELBO, and an analytically tractable solution cannot be derived. To address this problem, we propose an extended version of the SVI framework with more flexibility, namely, the extended SVI (ESVI) framework. This framework can be used in many non-Gaussian statistical models. First, some approximation strategies are applied to further lower the ELBO to avoid intractable moment calculations. Then, stochastic optimization with noisy natural gradients is used to optimize the lower bound. The excellent performance and effectiveness of the proposed method are verified in real data evaluation.

A key objective of software testing is to find program errors that cause failure in software, at less cost. One basic testing technique is random testing (RT), but many researchers have criticised its failure‐detection effectiveness. Several researchers have proposed that an enhancement of the failure‐detection effectiveness of RT is achieved if test cases are evenly spread within the input domain. Adaptive RT (ART) describes a family of algorithms that employ various strategies to evenly and randomly spread test cases. Fixed sized candidate set ART (FSCS‐ART) is an ART algorithm that has gained many research studies far and wide; however, the high distance computations make its algorithm computationally expensive. The authors propose a new ART method that restricts distance computations to only test cases inside an exclusion zone. The experimental results show that the new ART method not only improves RT but also provides failure‐detection effectiveness similar to FSCS‐ART, while significantly minimising computation overhead.