Rough K-Means (RKM) is an efficient clustering algorithm for overlapping datasets, and has captured increasing attention in recent years. Rough k-means algorithms in the main focus on the further description of uncertain objects located in boundary regions in order to improve the performance. However, most available RKM algorithms fail to pay attention to the influence of imbalanced clusters, together with imbalanced spatial distributions (i.e., the cluster density) and differing cluster sizes (i.e., the number of object ratios). This paper seeks to address this deficiency and examines in detail some adverse effects caused by imbalanced clusters. To mitigate adverse effects of imbalanced clusters and decrease the computational cost, an interval type-2 fuzzy local measure for the rough k-means clustering is proposed, on the basis of which, a novel rough k-means clustering algorithm has been developed which specifically gives due consideration to imbalanced clusters. The effectiveness and superiority of this algorithm is demonstrated through simulation and experimental analysis.

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations which are fundamentally dynamic. To address this failing, this paper proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors which exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrates the validity of the secondary dynamic adjustment strategy for online learning, while also providing higher accuracy levels from the prediction model.Impact Statement-Reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. A large number of PV power prediction methods have been developed. However, there are still a number of key challenges. Especially, the prediction effect of forecasting models that have merely undergone offline training are prone to decline due to various changes, which necessitating that the models ultimately need to be retrained. This paper makes significant contributions to solving the existing problems of neural-network-based PV power prediction models. This achievement enhances the forecasting model that can effectively adapt to the dynamic variations of the PV power without the need for retraining.

In cross-modal retrieval, the hashing technique has sparked a great revolution because of its competitive query speed and minimal storage. However, existing approaches may have critical limitations: 1) Label Intrinsic Relations. They barely explore category information inherent in labels and only consider labels as distinct entities, losing rich latent semantic information. 2) Modality-specific and Modality-coherence Semantics. They often construct a common subspace and an affinity matrix to learn modality-specific features and modality-coherence correlations, respectively. The former will lead to considerable quantization errors because the subspaces should be approximate rather than exactly equal. The latter is not scalable due to its high computational costs. 3) Non-relaxation Optimization Strategy. To solve constraints, some approaches relax the binary constraints to continuous, rising significant quantization errors. To mitigate these problems, we propose Disperse Asymmetric Subspace Relation Hashing, termed DASRH. In particular, it first embeds modality-specific kernel features into dispersed latent spaces, which can effectively fuse heterogeneous patterns. Additionally, it exploits fine-grained categories from labels by reconstructing collective semantic representations, making discriminative binary codes. Furthermore, it constructs an asymmetric consistent relation integration, preserving both inter-modal disparities and intra-class differences. In the optimization process, an effective alternative iterative optimization scheme is established. Theoretical analysis and comprehensive experiments highlight the advantages of our DASRH against cutting-edge technology.

Information granularity and information granules are fundamental concepts that permeate the entire area of granular computing. With this regard, the principle of justifiable granularity was proposed by Pedrycz, and subsequently a general two-phase framework of designing information granules based on Fuzzy C-means clustering was successfully developed. This design process leads to information granules that are likely to intersect each other in substantially overlapping clusters, which inevitably leads to some ambiguity and misperception as well as loss of semantic clarity of information granules. This limitation is largely due to imprecise description of boundary-overlapping data in the existing algorithms. To address this issue, the rough $k$ -means clustering is introduced in an innovative way into Pedrycz’s two-phase information granulation framework, together with the proposed local boundary fuzzy metric. To further strengthen the characteristics of support and inhibition of boundary-overlapping data, an augmented parametric version of the principle is refined. On this basis, a local boundary fuzzified rough $k$ -means-based information granulation algorithm is developed. In this manner, the generated granules are unique and representative whilst ensuring clearer boundaries. The validity and performance of this algorithm are demonstrated through the results of comparative experiments.