Accurate time series forecasting is essential for proactive management and resource allocation in intelligent IoT systems, where data are often affected by sensor noise, multi-scale periodicity, and cross-frequency interactions. Although decomposition-based frameworks are widely adopted, most existing approaches rely on time-domain modeling with limited nonlinear capacity or treat frequency decomposition as a generic preprocessing step. Such designs fail to align model inductive bias with the intrinsic multi-scale structure of seasonal dynamics, leading to incomplete seasonal representations and degraded forecasting performance. To address this limitation, we propose Detail-aware Wavelet Decomposition and Mixing (DWDMixer), a seasonal-centric forecasting architecture built on multi-scale representation learning and seasonal-trend decomposition, where trend components serve as complementary signals. To enable fine-grained seasonal modeling, we propose a Detail-aware Wavelet Transform Hybrid (DWTH) block that performs hierarchical wavelet decomposition guided by detail-sensitive representations. DWTH captures cross-scale and cross-frequency interactions while jointly modeling temporal and spectral information, yielding expressive multi-resolution representations. Extensive experiments on 10 benchmark datasets demonstrate that DWDMixer significantly outperforms state-of-the-art models, achieving error reductions of up to 9% in MSE for long-term forecasting and 22% in MAE for short-term forecasting. Our code is available at https://github.com/zpc2002zpc/DWDMixer.

Multi-party urban flow analysis is a crucial task in smart cities. However, existing analysis methods has difficulty in trade-off between data privacy security and spatio-temporal feature capture. The solution to the problem of how to capture the complete spatio-temporal features of multi-party urban flow data while protecting data privacy is of great importance in multi-party urban flow analysis. Therefore, to address data privacy and spatio-temporal feature capture in multi-party urban flow analysis, this paper proposes a spatio-temporal federated analysis model, for multi-party urban flow mining, which is able to effectively protect data privacy and capture spatio-temporal features completely at the same time. Firstly, a multi-party urban flow mining framework based on federated learning is proposed to realize complete capture of spatio-temporal feature information of multi-party urban flow data and mining urban flow pattern knowledge under the premise of protecting data privacy. Secondly, to address the communication cost of the multi-party urban flow analysis, we propose a lazy aggregation method based on similarity clustering, which improves the communication efficiency between clients and the server. Further, we propose a similarity evaluation criteria for urban flow data based on step function, which can effectively calculate the similarity between urban flow data. Finally, we compare the proposed model with some benchmark methods on Chengdu Didi order data and point of interest data to prove the effectiveness of the proposed model and visualize and analyze the spatio-temporal features.

Data missing is a common issue in real-world applications, posing significant challenges for incomplete data processing. Traditional incomplete multi-view clustering methods rely on manually-designed optimization problems based on prior interpretable knowledge, considering the full utilization of available data. However, their limited feature extraction capability may become a bottleneck. In contrast, deep optimization methods leverage learning-based nonlinear transformations for clustering. They primarily achieve data imputation through the generalization ability of deep models, but their model interpretability may be limited by the black-box nature. Moreover, most existing methods only explore the structure of each view independently, where these structures are fixed and cannot form a complete unified structure. To address these issues, we propose a Structural Optimization-inspired Interpretable Network (SOI-Net) for incomplete multi-view clustering. Specifically, we project the features of all views into a unified representation space with the un-missing information of the views as constraints. By optimizing consistent structural information, we preserve the structures of missing modalities in the unified representation space, thereby mitigating the impact of missing data. Meanwhile, we derive network components based on the optimization problem to guide the learning of structure and representation. The practical significance of these network components provides model design-level interpretability. Extensive experiments on six datasets validate the effectiveness of SOI-Net in handling incomplete multi-view clustering task.

Multi-view unsupervised feature selection (MUFS) has received considerable attention in recent years. Existing MUFS methods for processing unlabeled incomplete multi-view data, where some samples are missing in certain views, first impute the missing values and then perform feature selection on the completed dataset. However, treating imputation and feature selection as two separate processes overlooks their potential interactions. The graph-guided local structure gleaned from feature selection can aid in imputation, which in turn can enhance the feature selection performance. Additionally, most similarity graph-based MUFS methods suffer from high computational costs. To address these problems, we propose a novel MUFS method, termed Tensorial Incomplete Multi-view unsupErvised Feature Selection (TIME-FS). TIME-FS unifies missing value recovery, discriminative feature selection, and low-dimensional representation learning within a joint framework through matrix decomposition. Then, TIME-FS conducts CP decomposition on tensor data formed by the low-dimensional representations of different views to learn a consistent anchor graph across views and a view-preference weight matrix, both of which simultaneously guide missing view imputation and feature selection. Furthermore, an efficient algorithm with low time complexity and rapid convergence is proposed to solve TIME-FS. Extensive experimental results demonstrate the effectiveness and efficiency of TIME-FS over state-of-the-art methods.

Accurate user profiles are crucial for personalized recommendation systems to mitigate information overload on large-scale online platforms. While recent advances in large language models have enhanced semantic understanding for profile construction through textual artifacts, existing methods often neglect the higher-order structural patterns inherent in user-item interaction graphs-a key limitation for achieving accurate and diverse recommendations. In this paper, we propose SSPRec, a Higher-order Structure and Semantics-enhanced User Profiling for Recommendation. Specifically, we first introduce a multi-hop proximity matrix over item-item transitions, followed by low-rank approximation and clustering to group users based on behavioral similarity. Group-level user profiles are then distilled via representative keywords extracted from co-interacted items, and collaborative embeddings are concurrently learned from the interaction graph. To integrate collaborative signals with language-based profiles, we introduce a cross-view contrastive objective that encourages coherence between structural and semantic representations. Final recommendations are made using a fused user-item similarity score. Extensive experiments on four real-world datasets show that SSPRec not only outperforms baselines in accuracy (with 46.35% improvements), but also remains diverse and robust, even under incomplete interactions.

Air quality prediction utilizes IoT technologies to collect data centrally for model training, which may cause regulatory risks, privacy concerns, and high costs of integrating data. Meanwhile, distributed training through federated learning relies on the pre-defined graph structure generated by the geographic locations of air monitoring stations, which fails to capture the potential spatial relationships between air monitoring stations. In addition, the inherent multi-period attribute of air quality data makes time series changes extremely complex. To this end, this paper proposes a Federated framework for air quality prediction with Pre-defined graph and Adaptive Graph (FedPAG). Specifically, the client encodes the air quality data and meteorological data provided by the Internet of Things (IoT) system using the multi-period interaction and encoder modules to capture the proximity and periodicity features of the time series data. Next, the server combines the hidden states uploaded by the clients with pre-defined graph and adaptive graph respectively and forms node embeddings to capture the spatial features among the clients. Then, the client concatenates the hidden states with node embeddings to fuse the spatial information and feeds them into the decoder to obtain the final predicted values. Finally, we conduct experiments on the Beijing and Shijiazhuang datasets to demonstrate the effectiveness of the proposed method.

In the realm of data mining, outlier detection has emerged as a pivotal research focus, aimed at uncovering anomalies within datasets to extract meaningful and valuable insights. The objective is to leverage data mining methodologies to pinpoint anomalies within datasets, thereby revealing crucial and enlightening information. Herein, we introduce a groundbreaking outlier detection methodology, ODMGIS, that seamlessly integrates multigranularity representation and information set concepts to devise the multigranularity information set (MGIS) model. This model adeptly characterizes the distribution patterns of data points. First, we employ entropy function and their complementary function as measurement tools to accurately quantify the inherent uncertainty in data with different distributions, and considers the sum of the two as a comprehensive representation of the overall uncertainty of the information source. Subsequently, an outlier score model is constructed based on MGIS, which can deeply characterize the degree of outlierness of samples, thereby effectively identifying abnormal points in the dataset. During validation, ODMGIS was rigorously tested on practical datasets from medicine and bioinformatics, and its performance was benchmarked against both traditional and the state-of-the-art algorithms, showcasing substantial benefits. This research not only contributes a fresh perspective to outlier detection, but also sparks innovative avenues for exploring and advancing the granular computing theory.

In our daily lives, people frequently consider daily schedule to meet their needs, such as going to a barbershop for a haircut, then eating in a restaurant, and finally shopping in a supermarket. Reasonable activity location [or point-of-interest (POI)] and activity sequencing will help people save a lot of time and get better services. In this article, we propose a reinforcement learning-based deep activity factor balancing model to recommend a reasonable daily schedule according to user's current location and needs. The proposed model consists of a deep activity factor balancing network (DAFB) and a reinforcement learning framework. First, the DAFB is proposed to fuse multiple factors that affect daily schedule recommendation (DSR). Then, a reinforcement learning framework based on policy gradient is used to learn the parameters of the DAFB. Further, on the feature storage based on the matrix method, we compress the feature storage space of the candidate POIs. Finally, the proposed method is compared with seven benchmark methods using two real-world datasets. Experimental results show that the proposed method is adaptive and effective.

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on a deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate the superior performance of our framework, which is thus shown to have a great representation ability.

As the spatio-temporal data of a city is not always available, insufficient data would lead to poor performance in some urban prediction tasks. Existing works utilize transfer learning to solve the data scarcity problem, but they ignore the differences in data distributions across cities, which leads to the ineffectiveness of knowledge transfer. In this paper, we propose a domain adversarial model with knowledge transfer for spatio-temporal prediction across cities, entitledCityTrans. Specifically, 1) the self-adaptive spatio-temporal knowledge (namely ST-Knowledge) is mined, to learn the latent spatial and temporal patterns among cities; 2) the domain-adversarial training strategy is introduced to enhance domain invariance; 3) a knowledge attention mechanism is proposed to extract the transferable information from the ST-Knowledge. Note that our CityTrans is an end-to-end domain adversarial spatio-temporal network without two-stage training (i.e., pre-training and fine-tuning). Finally, we conduct extensive experiments on two spatio-temporal prediction tasks: traffic (flow and speed) prediction, and air quality prediction. Experimental results demonstrate that CityTrans outperforms state-of-the-art models on all tasks by a significant margin.

Representing the structural relations between entities, i.e., knowledge graph embedding, which is a method to learn low-dimensional representations of knowledge, has become an increasingly prevalent research orientation in cognitive and human intelligence. It is significant to study how to interrelate, fuse and embed the knowledge graph data from different domains while considering data not shared. In this paper, we propose a model of cross-domain knowledge graph embedding in federated learning (FedCKE), in which entity/relation embedding between different domains can interact securely in the case that data is not shared. In advance of client model training, we present an inter-domain encrypted entity/relation alignment method using the encrypted sample alignment method in vertical federated learning, which can obtain entity/relation intersections between different domains without revealing any triples structure and additional entities/relations in the respective datasets. On the server, we aggregate the same entity/relation embeddings by the association in conjunction with the parameter-secure aggregation method in horizontal federated learning. Experimental results on three real datasets show that the proposed FedCKE model is able to enhance the embedding of different clients (domains).

Multi-view clustering research is a hot topic in the field of data mining, where complementary information between views can better describe data objects and improve the clustering performance. Non-negative matrix factorization (NMF) based multi-view clustering algorithm suffers from weak feature extraction, slow convergence speed and low accuracy. To solve these problems, this paper proposes a generalized deep learning multi-view clustering (GDLMC) algorithm based on NMF. Firstly, via decoupling the elements in the matrix, the matrix elements are non-negatively restricted using an activation function with a non-negative value domain, and the elements are updated employing stochastic gradient descent with learning rate guidance. Then, the corresponding gradients when the elements update are transformed into generalized weights and generalized biases, followed by combining the generalized weights and generalized biases with activation functions to construct generalized deep learning (GDL). Further, GDL is adopted to learn the corresponding low-dimensional matrix of each view and consensus matrix for obtaining the GDLMC algorithm. In addition, the detailed reasoning of the GDLMC algorithm are given. Finally, extensive experiments are conducted on four public datasets including regular and large-scale datasets, and the experimental results show that GDLMC has significant advantages.

Feature selection has been studied by many researchers using information theory to select the most informative features. Up to now, however, little attention has been paid to the interactivity and complementarity between features and their relationships. In addition, most of the approaches do not cope well with fuzzy and uncertain data and are not adaptable to the distribution characteristics of data. Therefore, to make up for these two deficiencies, a novel interactive and complementary feature selection approach based on fuzzy multineighborhood rough set model (ICFS_FmNRS) is proposed. First, fuzzy multineighborhood granules are constructed to better adapt to the data distribution. Second, feature multicorrelations (i.e., relevancy, redundancy, interactivity, and complementarity) are considered and defined comprehensively using fuzzy multigranularity uncertainty measures. Next, the features with interactivity and complementarity are mined by the forward iterative selection strategy. Finally, compared with the benchmark approaches on several datasets, the experimental results show that ICFS_FmNRS effectively improves the classification performance of feature subsets while reducing the dimension of feature space.

Clustering is a popular research topic in the field of data mining, in which the clustering method based on non-negative matrix factorization (NMF) has been widely employed. However, in the update process of NMF, there is no learning rate to guide the update as well as the update depends on the data itself, which leads to slow convergence and low clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC) algorithm based on NMF is proposed in this article. Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the elements in the matrix guided by the learning rate. Then, the gradient values corresponding to the element update are transformed into generalized weights and generalized biases, by inputting the elements as well as their corresponding generalized weights and generalized biases into the nonlinear activation function to construct the GDLC algorithm. In addition, for improving the understanding of the GDLC algorithm, its detailed inference procedure and algorithm design are provided. Finally, the experimental results on eight datasets show that the GDLC algorithm has efficient performance.

Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires centralized data collection from individual users, which may be prohibited by data protection acts, such as GDPR and CCPA. In this paper, we address the problem of daily schedule recommendation utilizing the deep reinforcement learning model in a federated learning framework (FedDSR). And curriculum learning is applied to guide the training process towards better local optimization and better generalization. For the uploaded local parameters, a similarity aggregation algorithm is proposed to improve the quality of the model. The experimental results show that the proposed FedDSR model is superior and effective to multiple baselines on two real datasetsGeolifeandChengdu. Comparing with baselines, our method not only ensures that the parties do not need to share data and thus achieve joint modeling, but also can exceed$\sim\!\! 18\%$under evaluation metricperimeterand improve$\sim\! 0.72\%$under evaluation metricADTS.

Recommender system can provide users with the required information accurately and efficiently, playing a very important role in improving users' life experience. Although knowledge graph-based recommender system can solve the sparsity and cold start problems faced by traditional recommender system, it cannot handle the cross-domain cold start problem and cannot provide multi-domain recommendations. Therefore, this paper focuses on multi-domain item-item (I2I) recommendation based on cross-domain knowledge graph embedding by analyzing the association between items of the same domain and the interaction between items of diverse domains with the aid of knowledge graph that contains rich information. Firstly, a cross-domain knowledge graph chiasmal embedding approach is proposed to efficiently interact all items in multiple domains. To help achieve both homo-domain embedding and hetero-domain embedding of items, a binding rule is put forward. Secondly, a multi-domain I2I recommendation method is presented to efficiently recommend items in multiple domains, which is a recommendation method based on link prediction of knowledge graph. Finally, the proposed methods are compared and analyzed with some benchmark methods using two datasets. The experimental results show that the proposed methods achieve better link prediction results and multi-domain recommendation results.