Representative Clustering Research Articles

Time-series data clustering with load-shape preservation for identifying residential energy consumption behaviors

Categorizing residential energy demand patterns is a principal task for demand-side management (DSM) and energy-saving strategies. While deep learning (DL)-based clustering offers a promising alternative to conventional machine learning (ML), DL’s advantages and disadvantages over ML still remain unclear in identifying energy demand patterns. Moreover, prevalent DL-based clustering can suffer from catastrophic feature distortion when capturing load-shape information from energy-load data, leading to erroneous pattern identification. To address these issues, we propose integrating a load-shape preservation mechanism into representative DL-based clustering and investigate its effectiveness in categorizing energy demand patterns, compared to existing ML and DL. We experiment and compare the three clustering approaches using one-year residential energy-load data. Results show that the proposed DL, equipped with load-shape preservation, outperformed ML quantitatively and closely aligns with the baseline DL’s performance. This is particularly significant considering that the baseline DL prioritizes quantitative enhancements, sometimes compromising load-shape precision. Furthermore, the proposed DL discovered more diverse energy demand patterns than the baseline ML and DL, while producing more human-agreeable results. This finding underscores the pivotal role of load-shape preservation in enhancing data clustering and demand pattern recognition in the real-world. These benefits will facilitate personalized DSM interventions and foster residents’ energy-saving behaviors.

Analyzing daily change patterns of indoor temperature in district heating systems: A clustering and regression approach

Measuring the indoor temperature of building rooms is a valuable approach for evaluating thermal comfort and providing feedback control for heat substations in district heating systems (DHSs) in China. Previous studies on indoor temperatures have primarily focused on analyzing their overall trends and influencing factors, while research on daily change patterns is lacking. This study utilized a clustering method to analyze the indoor temperature data from an actual DHS in Northeast China. First, a 24-h observation vector was constructed using the deviation between the actual and target values to represent the daily temperature pattern. Second, the k-means method was applied to cluster the values, and the quantity distribution and typical characteristics of each cluster were analyzed. Finally, a multi-nominal logistic regression model was used to analyze the influence of different factors on each cluster. The comparison results with the four representative clustering algorithms indicated that k-means was the optimal model and the optimal number of clusters was 4. The trend of each cluster was roughly the same, with the main difference being the fluctuation amplitude and distance from the target value. The differences between the clusters were related to various influencing features, with the primary return pressure for workdays and the secondary return pressure for holidays being the most significant. This study identified the optimal daily variation patterns of indoor temperature and analyzed the important features that affect this pattern, which is beneficial for enhancing the regulatory efficiency of DHS.

Transfer-Learning-Based Gaussian Mixture Model for Distributed Clustering.

Distributed clustering based on the Gaussian mixture model (GMM) has exhibited excellent clustering capabilities in peer-to-peer (P2P) networks. However, more iterative numbers and communication overhead are required to achieve the consensus in existing distributed GMM clustering algorithms. In addition, the truth that it cannot find a closed form for the update of parameters in GMM causes the imprecise clustering accuracy. To solve these issues, by utilizing the transfer learning technique, a general transfer distributed GMM clustering framework is exploited to promote the clustering performance and accelerate the clustering convergence. In this work, each node is treated as both the source domain and the target domain, and these nodes can learn from each other to complete the clustering task in distributed P2P networks. Based on this framework, the transfer distributed expectation-maximization algorithm with the fixed learning rate is first presented for data clustering. Then, an improved version is designed to obtain the stable clustering accuracy, in which an adaptive transfer learning strategy is adopted to adjust the learning rate automatically instead of a fixed value. To demonstrate the extensibility of the proposed framework, a representative GMM clustering method, the entropy-type classification maximum-likelihood algorithm, is further extended to the transfer distributed counterpart. Experimental results verify the effectiveness of the presented algorithms in contrast with the existing GMM clustering approaches.

Cluster ensemble selection and consensus clustering: A multi-objective optimization approach

Cluster ensembles have emerged as a powerful tool to obtain clusters of data points by combining a library of clustering solutions into a consensus solution. In this paper, we address the cluster ensemble selection problem and design a multi-objective optimization-based solution framework to produce consensus solutions. Given a library of clustering solutions, we first design a preprocessing procedure that measures the agreement of each clustering solution with the other solutions and eliminates the ones that may mislead the process. We then develop a multi-objective optimization algorithm that selects representative clustering solutions from the preprocessed library with respect to size, coverage, and diversity criteria and combines them into a single consensus solution, for which the true number of clusters is assumed to be unknown. We conduct experiments on different benchmark data sets. The results show that our approach yields more accurate consensus solutions compared to full-ensemble and the existing approaches for most data sets. We also present an application on the customer segmentation problem, where our approach is used to segment customers and to find a consensus solution for each segment, simultaneously.

Using Dual Spatial Clustering Models for Urban Fringe Areas Extraction Based on Night-time Light Data: Comparison of NPP/VIIRS, Luojia 1-01, and NASA’s Black Marble

Night-time light data (NTL) have been extensively utilized to map urban fringe areas, but to date, there has not been a comprehensive evaluation of the existing spatial clustering methods for delineating the urban fringe using different types of night-time light data. Therefore, we first selected three popular sources of night-time light data (i.e., NPP/VIIRS, Luojia 1-01, and NASA’s Black Marble) to identify the urban fringe. The recognition of spatial mutations across the urban–rural gradient was conducted based on changes in night light intensity using a spatial continuous wavelet transform model. Then, we employed three representative dual spatial clustering approaches (i.e., MK-Means, DBSC, and DSC) for extracting urban fringe areas using different NTL. By using dual spatial clustering, the spatial patterns of the mutation points were effectively transformed into homogeneous spatially adjacent clusters, enabling the measurement of similarity between mutation points. Taking Nanjing city, one of China’s megacities, as the study area, we found that (1) Compared with the fragmented and concentrated results obtained from the Luojia 1-01, NASA’s Black Marble and NPP/VIIRS data can effectively capture the abrupt change of urban fringes with NTL variations; (2) DSC provided a reliable approach for accurately extracting urban fringe areas using NASA’s Black Marble data.

Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering

Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates comparable performance for knowledge exploitation under the circumstance of information incompleteness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters which are challenging to be determined for different data types. This paper proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modelling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.

A semi-supervised hierarchical ensemble clustering framework based on a novel similarity metric and stratified feature sampling

Recently, both ensemble clustering and semi-supervised clustering have emerged as important paradigms of traditional clustering. Ensemble clustering seeks to integrate multiple clustering results from different methods or the same methods with different parameters. Semi-supervised clustering involves using a small amount of class membership information in some samples for the learning process. Meanwhile, Semi-Supervised Ensemble Clustering (SSEC) has attracted increasing attention due to its high performance. However, most SSEC algorithms are configured based on partitional clustering techniques, and there are few attempts on hierarchical clustering techniques. Even in existing hierarchy-based SSEC algorithms, prior knowledge is not sufficiently used and is often applied to create primary partitions. To address these problems, we propose a Semi-supervised Hierarchical Ensemble Clustering framework based on a novel Similarity metric and stratified feature Sampling, which we call SHECSS. SHECSS uses the information of all primary partitions according to their strength to calculate the similarity between samples. Also, SHECSS is equipped with a stratified feature sampling mechanism that can improve the diversity of primary partitions and deal with high-dimensional data. Here, the primary partitions are created based on multiple hierarchical clustering techniques, and the target partition is configured by a consensus function based on the clusters clustering policy. Experimental results show the effectiveness and efficiency of SHECSS compared to representative clustering methods.

From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data are not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn the cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs in clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracked the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborated on the main ideas, technical details, advantages, and disadvantages of different types of clustering methods of HSIs. We provided a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provided a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models.

Visual analysis of the current research status and development of burn-related coagulation dysfunction

Objective: To conduct a visual analysis of the literature on burn-related coagulation dysfunction and to explore the current research status, evolution process, hot topics, and future research trends in burn-related coagulation dysfunction at home and abroad. Methods: The bibliometrics method was used. The literature on burn-related coagulation dysfunction which were published in Web of Science and China National Knowledge Internet databases from January 1, 1950 to May 1, 2022, and met the inclusion criteria were retrieved for publication volume analysis. The literature on burn-related coagulation dysfunction were retrieved as above in the core collection of Web of Science and China National Knowledge Internet databases, and CiteSpace 5.8.R3 software was used to perform co-occurrence analysis, cluster analysis, and literature co-citation analysis of key words. Results: A total of 501 and 235 literature on burn-related coagulation dysfunction were retrieved from Web of Science database and China National Knowledge Internet database, respectively. The literature on burn-related coagulation dysfunction emerged from 1975 and 1950, respectively, in China and abroad, which were gradually increased later. The frequency and centrality of Chinese key words such as , , were high in 235 literature in China National Knowledge Internet database, and the frequency and centrality of key words such as burn, coagulation, and deep vein thrombosis were high in 340 literature in the core collection of Web of Science database. In China National Knowledge Internet database, the top 6 Chinese key words in terms of burst intensity were , , , , , , and the first 3 among which were burst key words in the early stage; and in the core collection of Web of Science database, the key words with higher burst intensity were disseminated intravascular coagulation and pulmonary embolism, which were the burst key words in the early stage. The representative clustering labels in China National Knowledge Internet database were #0 , #1 , and #2 , etc., and the representative clustering labels in the core collection of Web of Science database were #0 risk, #1 surgical patient, and #2 sepsis. Early researches in China National Knowledge Internet database and the core collection of Web of Science database focused on the presence of burn-related coagulation dysfunction itself, while the late researches focused on the relationship between burn-related coagulation dysfunction and inflammation, immunity, coagulation in general, and wounds. From 2010 onwards, there were a large number of core cited literature in the core collection of Web of Science database, and the prevention and treatment of vein thromboembolism was the most popular research direction in recent years. The researches on optimization and standardization of diagnostic methods and the overall mechanism of burn-related coagulation dysfunction would be the main research directions in the future. Conclusions: The research hotspots and evolution processes of burn-related coagulation dysfunction at home and abroad have both similarities and differences, and the current research hotspot is the relationship between coagulation and inflammation, immunity. With researches increasingly deepening, the researches on optimization and standardization of diagnostic methods and the overall mechanism of burn-related coagulation dysfunction will be the main research directions in the future.

S2DMSC: A Self-Supervised Deep Multilevel Subspace Clustering Approach for Large Hyperspectral Images

Subspace clustering (SC) has achieved remarkable success in hyperspectral images (HSIs) due to the powerful representation ability of handling high-dimensional complex data. However, most of the existing SC methods focus on linear subspace representation and ignore the more effective nonlinear representation. Besides, SC suffers from the bottlenecks, such as high computation load and memory capacity, due to the spectral decomposition of adjacency matrix for large HSIs. To overcome these limitations, we propose an end-to-end learnable network framework for large HSIs, called self-supervised deep multi-level subspace clustering (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DMSC), which incorporates the convolutional neural network (CNN) module, multi-level subspace clustering (MSC) module, and high-quality pseudo-label-based self-supervised learning module into a unified learning framework. More concretely, the deep multi-level spatial-spectral representation from hierarchical superpixels is modeled as a sparsity-constrained self-expression module for SC to construct high-quality pseudo-labels to learn the network parameters and produce better clusters for hyperspectral pixels. Experimental results on four classical HSIs demonstrate the effectiveness of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DMSC and exhibit superior clustering performance compared to the representative clustering methods.

Deep Embedded Clustering Framework for Mixed Data

Deep embedded clustering (DEC) is a representative clustering algorithm that leverages deep-learning frameworks. DEC jointly learns low-dimensional feature representations and optimizes the clustering goals but only works with numerical data. However, in practice, the real-world data to be clustered includes not only numerical features but also categorical features that DEC cannot handle. In addition, if the difference between the soft assignment and target values is large, DEC applications may suffer from convergence problems. In this study, to overcome these limitations, we propose a deep embedded clustering framework that can utilize mixed data to increase the convergence stability using soft-target updates; a concept that is borrowed from an improved deep Q learning algorithm used in reinforcement learning. To evaluate the performance of the framework, we utilized various benchmark datasets composed of mixed data and empirically demonstrated that our approach outperformed existing clustering algorithms in most standard metrics. To the best of our knowledge, we state that our work achieved state-of-the-art performance among its contemporaries in this field.

An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data

Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algorithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propagation and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strategy to enhance local search and convergence efficiency. The benchmark function experiments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness.

Researches Advanced in Clustering Algorithms

Clustering is a technique to find the intrinsic structure between data and is a fundamental problem in many data-driven application fields. Currently, clustering is generally modeled as an unsupervised learning task, aiming to mine similar features between different samples and cluster samples with similar features into clusters. Ideally, objects in the same cluster are expected to be similar in the clustering results, while objects in different clusters are quite different. This study summarizes the research status of clustering algorithms in recent years. Specifically, the relevant critical steps of clustering algorithms are first introduced. From two aspects of partition and hierarchical clustering, representative clustering algorithms such as K-means, K-medoids, CLARANS, BIRCH, DBSCAN, and CURE are further detailed. This study also analyzes and summarizes the above algorithms in terms of critical technologies, algorithm ideas, benefits, and shortcomings and compares the distance accuracy of different algorithms on standard data sets. The above work will provide a valuable reference for cluster analysis and data mining research.

Fully automatic operational modal analysis method based on statistical rule enhanced adaptive clustering method

Timely monitoring of modal parameters is commonly adopted to keep in-service bridges safe. For fulfilling the demand, plenty of operational modal analysis (OMA) methods were developed in which only the structural response needs to be measured. However, when utilizing these methods, there are still some deficiencies, like mingled spurious modes and the hyper-parameters needing manual tuning. Although many studies have utilized clustering algorithms to solve these issues and promote the automatic level of the OMA method, some new hyper-parameters belonging to these algorithms would be introduced as well, which also need manual maneuvering. Meanwhile, the performance of various clustering algorithms on this issue also shows variety due to their distinct inductive biases. As a result, the prerequisite of relevant expertise still hinders the common users in their extraction and tracking of the bridge’s modal information. Under these circumstances, after conducting a comparison among representative clustering algorithms, this study proposed an applicable fully automatic OMA method by an adaptive clustering method, “K-average nearest neighbor density-based spatial clustering of applications with noise (KANN-DBSCAN)” enhanced by the five-number summary. This method’s performance was investigated via numerical analyses and the measured data from an actual bridge. The results illustrated that this method functions well in the tested scenarios and has the potential for wide application in actual engineering.

지하철역사 에너지성능 평가를 위한 클러스터링 분석기법을 활용한 지하철역사 대표모델 선정에 관한 연구

In order to realize the government’s plan to increase zero energy early and play a leading role in the low carbonization of railway facilities, Since 2025, the National Railroad Corporation has established a Zero Energy Roadmap for railway stations to promote zero energy building certification. However, currently, standards and tools for evaluating various railway history, including subways, are insufficient in Korea. In particular, railway history has characteristics that are distinct from general buildings, such as the presence of peak loads, continuous erosion, and the possession of safety and functional elements that must be equipped Accordingly, an alternative is needed to overcome the limitations of existing domestic energy performance evaluation tools developed for general building evaluation. Therefore, this paper uses the clustering technique as the first step to develop a decision-making tool that presents the energy performance evaluation and improvement direction of railway history It can represent the characteristics of subway history. In order to select a representative model, data collection and clustering analysis of all 291 subway stations in the Seoul area were performed using Open Data provided by the public data portal. K-means and GMM algorithms were used as clustering analysis methods. As a result of clustering analysis, the K-means algorithm was difficult to use as a result of classification because the data columns were diverse and were not in the form of circular distribution by cluster. Compared to K-means, GMM algorithms can significantly distinguish characteristics such as area, completion year, and platform type the results were shown.BRThe representative standard stations selected based on the results of GMM clustering were Konkuk University Station (Line 2), Suraksan Station, and Hongdae Station. After that, through simulation of representative models and multiple regression analysis using results, the standard model is developed and used as a tool for initial decision-making such as zero energy construction plan and remodeling plan for subway stations.

TECM: Transfer learning-based evidential c-means clustering

As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions. However, compared with other partition-based algorithms, ECM must estimate numerous additional parameters, and thus insufficient or contaminated data will have a greater influence on its clustering performance. To solve this problem, in this study, a transfer learning-based ECM (TECM) algorithm is proposed by introducing the strategy of transfer learning into the process of evidential clustering. The TECM objective function is constructed by integrating the knowledge learned from the source domain with the data in the target domain to cluster the target data. Subsequently, an alternate optimization scheme is developed to solve the constraint objective function of the TECM algorithm. The proposed TECM algorithm is applicable to cases where the source and target domains have the same or different numbers of clusters. A series of experiments were conducted on both synthetic and real datasets, and the experimental results demonstrated the effectiveness of the proposed TECM algorithm compared to ECM and other representative multitask or transfer-clustering algorithms.

EGMM: An evidential version of the Gaussian mixture model for clustering

The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGMM (evidential GMM), in the theoretical framework of belief functions to better characterize cluster-membership uncertainty. With a mass function representing the cluster membership of each object, the evidential Gaussian mixture distribution composed of the components over the powerset of the desired clusters is proposed to model the entire dataset. The parameters in EGMM are estimated by a specially designed Expectation–Maximization (EM) algorithm. A validity index allowing automatic determination of the proper number of clusters is also provided. The proposed EGMM is as simple as the classical GMM, but can generate a more informative evidential partition for the considered dataset. The synthetic and real dataset experiments show that the proposed EGMM performs better than other representative clustering algorithms. Besides, its superiority is also demonstrated by an application to multi-modal brain image segmentation.

Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks.

In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. In addition, for event-driven traffic, the long delay incurred by clustering and searching for available routes after events results in poor timeliness of information transmission. In order to solve above problems, a traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP) is proposed in this paper. For the first time, time-triggered and event-driven traffic can be served by a single clustering protocol. To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. In this case, time-triggered traffic can be periodically served. Priority-based schedule and corresponding frame structure are designed to ensure priority delivery of event-driven information. The clustering architecture built for time-triggered traffic is leveraged, and there is no cluster construction and route selection after emergent events. Only the CRSNs nodes which discover emergent events and corresponding CHs participate in data transmission, which means that TD-IMOCRP covers fewer nodes, especially when the sink is located at the corner. Therefore, it can help reduce node energy consumption and delay. Simulation results demonstrate that compared with representative event-driven clustering protocols, TD-IMOCRP can decrease the average number of covered nodes and the total energy consumption by more than 66.3% and 25%, respectively. In addition, when serving time-triggered traffic, TD-IMOCRP can achieve almost the same performance as its basic version IMOCRP which is better than majority of current time-triggered clustering protocols. In a word, TD-IMOCRP can guarantee in-time delivery of event-driven information while guaranteeing its performance of serving time-triggered traffic.

A new shape-based clustering algorithm for time series

Time series clustering is a research hotspot in data mining. Most of the existing clustering algorithms combine with the classical distance measure which ignore the offset of sequence shape. As a result, shape-based clustering algorithms are becoming increasingly popular. On the majority of data sets, the most representative shape-based clustering algorithm, KShape, which defines a shape-based distance with shift invariance, has been shown to outperform other algorithms.In this paper, we propose a new shape-based clustering algorithm named Fractional Order Shape-based k-cluster(FrOKShape), which defines a multi-variable shape-based distance by normalized fractional order cross-correlation and uses the DTW Barycenter Averaging (DBA) as a center computation strategy. Our distance exhibits excellent shape shift deviating properties and good compatibility integrated with a variety of existing clustering center strategies so that it can provide more potential good results. Experiments show that combining our distance with a traditional clustering algorithm produces excellent clustering indicators. In a series of comparative experiments, FrOKShape also exhibits a comparable result to the existing better shape-based clustering algorithm KShape.

Privacy protection model considering privacy-utility trade-off for data publishing of weighted social networks based on MST-clustering and sub-graph generalization

Along with the rapid development of the Internet technology, social network sites (SNSs) are increasingly emerging. As various types of datasets are easily exposed to the network, privacy security in SNS becomes the focus of the study. Anonymization techniques, such as generalization and bucketization, and data perturbation techniques are usually employed in data publishing for privacy protection. However, in the meanwhile, the privacy protection methods reduce the utility of the data. Thus, the issue of privacy-utility trade-off becomes one very real problem. By considering the privacy-utility trade-off and combining the minimum spanning tree (MST) clustering technique and the sub-graph generalization technique, a privacy protection model is proposed for data publishing in weighted social networks. The MST-based clustering method is one representative and concise graph-based clustering algorithm. Meanwhile, as one MST of an edge-weighted graph is the minimum weight spanning tree, some important properties of the graph like the shortest path are preserved in the MST. Finally, on the basis of sub-graphs (clusters) divided by the MST-clustering, three sub-graph generalization approaches are designed in one reasonable way for data publishing to guarantee the privacy-utility trade-off of the proposed method. Experiments and result analysis indicate workability of the proposed method that the proposed model offers privacy protection for the data publishing by means of the sub-graph generalization approach while it can also improve the data utility especially for occasions like data mining, machine learning, and pattern recognition.