Density-based Spatial Clustering Of Applications With Noise Clustering Research Articles

Membership determination in open clusters using the DBSCAN Clustering Algorithm

Membership determination in open clusters using the DBSCAN Clustering Algorithm

Wind power forecasting method of large-scale wind turbine clusters based on DBSCAN clustering and an enhanced hunter-prey optimization algorithm

As the large-scale grid connection of wind turbines poses challenges to the safe and stable operation of the power grid, it is necessary to forecast the power of wind turbine clusters. However, with the rapid increase in the installed capacity of wind turbines, simultaneously improving the accuracy and efficiency of large-scale wind-turbine cluster power prediction models has become a challenge. In this research, a novel hybrid power-forecasting model for wind-turbine clusters has been designed using density-based spatial clustering of applications with noise (DBSCAN) and an enhanced hunter-prey optimization algorithm (ENHPO). First, the Pearson correlation coefficient was used to select multiple variables that significantly affected the wind power. Second, multiple wind turbines are clustered into different groups using the DBSCAN clustering algorithm, and ENHPO is employed to optimize the DBSCAN parameters to strengthen the clustering performance. Finally, one wind turbine with a high correlation was selected as the representative wind turbine in each group, and power prediction was achieved using the multivariate long short-term memory. Simulation results of four datasets in different seasons show that, compared with other clustering methods, such as fuzzy C-means, balanced iterative reduction, and clustering using hierarchies, K-means, and density peak clustering, the prediction accuracy and prediction efficiency of the proposed hybrid prediction model are improved by 21.85% and 18.07%, respectively, on average. The experimental results effectively demonstrate that the designed model can enhance the accuracy and efficiency of power forecasting simultaneously.

The Performance Comparison of DBSCAN and K-Means Clustering for MSMEs Grouping based on Asset Value and Turnover

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country’s economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability.   Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover

Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches

Cluster analysis can be used to organize samples and generate ideas regarding the multivariate geochemistry of given dataset. Traditional clustering techniques have the drawbacks of high computational complexity and poor adaptability to big data. Hence, there has recently been much focus on creating better clustering algorithms. Although many clustering algorithms have been applied, and some produce notable clustering results, the performance efficiency of algorithms is often highly dependent on the values the user chooses for the parameters. Currently, density-based spatial clustering of applications with noise clustering (DBSCAN) is widely utilized in image processing, bioinformatics, and social network analysis owing to its ability to detect clusters of various shapes. Even though partitional clustering techniques may be effective when the number of clusters K is known in advance, they cannot implement non-convex clustering and rapidly return to a local optimum. This study proposes the concept of DBSCAN clustering for stream sediment geochemical data. In this respect, the geochemical data collected from Varcheh district, SW Iran, were processed using the clr transformation before applying DBSCAN. Then, PCA was used to minimize the dimension of variables and specify the mineralization-related elements. In the following, one of the PCs connected with mineralization (PC2) was chosen for further analysis. DBSCAB, Mean-shift and Fuzzy K-means algorithms were used to monitor the multi-element geochemical anomalies linked to MVT PbZn deposits in the study area. According to Davies-Bouldin and Silhouette as two validation metrics, it can be deduced that the three SCB models are advantageous, however, the model generated by DBSCAN is preferable to the model generated by Mean-shift and Fuzzy K-means.

Extraction of Tooth Cusps based on DBSCAN Density Clustering and Neighborhood Search Algorithm.

A good tooth cusp extraction is helpful in evaluating the effect of cosmetic dental work in virtual tooth surgery. We propose a new tooth cusp extraction, which integrates the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm with the neighborhood search algorithm to extract tooth cusp from a three-dimensional cloud-point tooth model. This method used the point cloud height and curvature to screen out the dented point set. Then we employ the DBSCAN clustering algorithm to segment different feature regions of the tooth surface and generate the candidate point set. Finally, the candidate point set was accurately located at the tooth apex through the neighborhood search algorithm and the traversal search method of non-maximum suppression. The experimental results show that the proposed method is superior to the traditional watershed algorithm-based methods by calculating the recall rate and accuracy rate, and also has higher extraction speed and extraction precision than manual extraction methods.

Enhancing Business Sustainability through Stock Analysis: A Machine Learning Approach

This study explores the integration of machine learning methodologies in stock analysis to enhance the understanding of the relationship between sustainable business practices and financial performance. Against the backdrop of a shifting investment landscape that emphasizes responsible and informed decision-making, our research addresses the need for innovative approaches in evaluating stocks within a sustainability framework. Leveraging a combination of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and linear regression, we systematically analyze a dataset comprising sustainability metrics and stock performance. The DBSCAN clustering identifies distinct groups of stocks based on sustainability profiles, offering novel insights into market segmentation. Concurrently, linear regression models quantitatively reveal the impact of sustainability metrics on stock outcomes. The results affirm the significance of sustainability considerations in investment decisions, presenting a compelling case for the adoption of machine learning techniques in responsible investing strategies.

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors.

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields.

Spatial distribution characteristics and influencing factors of intangible cultural heritage in the Yellow River Basin

Given the background of ecological protection and high-quality development strategies in the Yellow River Basin (YRB), studying the spatial distribution characteristics and influencing mechanisms of intangible cultural heritage (ICH) in the YRB is of great significance for objectively understanding the entire process of cultural occurrence, evolution, protection, and development in the YRB. Considering 787 counties in nine provincial areas of the YRB as the research area, this study adopted density-based spatial clustering of applications with noise (DBSCAN), kernel density estimation (KDE), and GeoDetector, and explored the spatial distribution characteristics and influencing mechanisms of 892 national-level ICH items. The results showed that the entire ICH was spatially agglomerated in the YRB. Concurrently, regional differences were apparent, mainly concentrated in the middle–lower reaches and several cultural areas. Although all 10 ICH types showed spatial gathering characteristics, the degree of agglomeration of folk art and literature, traditional sports recreation, and acrobatics was relatively low. DBSCAN clustering showed that the middle and lower reaches of the Yellow River were the core areas of ICH and various types of ICH clustering. The kernel density also showed an agglomeration distribution trend and formed a “3 + 4 + 5” kernel density hierarchy, exhibiting prominent “center–periphery” characteristics. Among the natural environmental, socioeconomic, and historical–cultural factors, the socioeconomic factors significantly affected ICH spatial distribution, whereas natural environmental factors were less influential. The gross domestic product (GDP), national historical and culturally famous towns and villages, populations, and museums strongly influenced ICH spatial distribution. The factor interaction explanatory force was greater than that of a single factor, indicating synergistic effects between the factors. The explanatory force of GDP, urbanization level, population, minority population, and traffic interaction with other factors on ICH spatial distribution significantly improved.

Coarse-to-fine segmentation of individual street trees from side-view point clouds

Segmenting individual street trees from a street side-view point cloud is the first and key step of obtaining a street tree inventory. Using the classification-segmentation framework for individual tree segmentation makes tree detection simple and accurate, but segmenting overlapping trees is still challenging. To more accurately segment overlapping trees, a coarse-to-fine method for segmenting individual street trees from a side-view point cloud is proposed in this paper. Following the classification-segmentation framework, the tree points are first detected from the side-view street point cloud by a pointwise classifier fused from 13 local geometric features and then trained using random forest (RF). Second, the tree proposals are obtained by density-based spatial clustering of applications with noise (DBSCAN) clustering and detection error filtering. Third, the overlapping tree proposals are recognized by trunk identification, and the single tree proposals are directly output as individual trees. Fourth, the overlapping trees are roughly divided into individual tree proposals through vertical planes. Finally, individual trees with optimized contours are obtained by iteratively using DBSCAN clustering and k-nearest neighbor (k-NN) classification. The side-view point cloud of a 290 m-long urban street containing 77 street trees is captured by a hand-held mobile ZEB Horizon laser scanner. The tree detection attained an F1 score of 0.9916 with a precision of 0.9989 and a recall of 0.9864. For individual tree segmentation, the F1 score was 0.9745 with a precision of 0.9672 and a recall of 0.9819. Compared to two current classification-segmentation methods, the overlapping tree segmentation F1 scores were increased by 0.0914 and 0.0617, respectively. The proposed method can be applied to tree parameter extraction, which is an important urban forest inventory task and is crucial for urban forest management. In our experiment, the root mean squared error (RMSE) of the trunk diameter at breast height (DBH) estimation was 0.8485 cm.

Utilize DBN and DBSCAN to detect selective forwarding attacks in event-driven wireless sensors networks

Malicious nodes launching selective forwarding attacks jeopardize network reliability in event-driven wireless sensor networks. Swift detection and exclusion of these nodes are vital, especially in harsh environments where normal nodes also suffer from decreased forwarding rates due to poor channel conditions. To solve this problem, this paper proposes a deep belief network (DBN) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based detection scheme against selective forwarding attacks under harsh environments. The DBN algorithm extracts and analyzes behavior features of nodes, while the DBSCAN clustering algorithm effectively distinguishes between malicious and normal nodes during selective forwarding attacks. Simulation results demonstrate the scheme’s effectiveness with a missed detection rate (MDR) of approximately 2% and a false detection rate (FDR) below 5% in harsh environments, providing a robust solution for ensuring the integrity and efficiency of data transmission in event-driven wireless sensor networks.

Research on WPD and DBSCAN-L-ISOMAP for circuit fault feature extraction

Abstract To solve the problem of feature extraction in electronic circuits due to the nonstationary and nonlinear characteristics of fault signals, a fault feature extraction method for electronic circuits is proposed, which combines wavelet packet analysis and an improved landmark ISOMAP mapping algorithm. The wavelet packet technology is used to decompose and reconstruct the fault feature signals at multiple levels. The extracted wavelet entropy is used to construct the feature vector matrix. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is used to calculate and screen the landmark points. The improved landmark ISOMAP is used to embed the high-dimensional fault feature parameter set into the low-dimensional eigenspace, extract the low-dimensional and sensitive fault feature subset, and apply the support vector machine to identify the fault. The fault diagnosis experiment of the three-phase VIENNA rectifier shows that compared with the principal component analysis method, the traditional ISOMAP method, and the landmark ISOMAP method, the landmark ISOMAP method based on DBSCAN clustering algorithm extracts the fault signal characteristics of electronic equipment more easily.

Accurate and real-time object detection in crowded indoor spaces based on the fusion of DBSCAN algorithm and improved YOLOv4-tiny network

Abstract Real-time object detection is an integral part of internet of things (IoT) application, which is an important research field of computer vision. Existing lightweight algorithms cannot handle target occlusions well in target detection tasks in indoor narrow scenes, resulting in a large number of missed detections and misclassifications. To this end, an accurate real-time multi-scale detection method that integrates density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and the improved You Only Look Once (YOLO)-v4-tiny network is proposed. First, by improving the neck network of the YOLOv4-tiny model, the detailed information of the shallow network is utilized to boost the average precision of the model to identify dense small objects, and the Cross mini-Batch Normalization strategy is adopted to improve the accuracy of statistical information. Second, the DBSCAN clustering algorithm is fused with the modified network to achieve better clustering effects. Finally, Mosaic data enrichment technique is adopted during model training process to improve the capability of the model to recognize occluded targets. Experimental results show that compared to the original YOLOv4-tiny algorithm, the mAP values of the improved algorithm on the self-construct dataset are significantly improved, and the processing speed can well meet the requirements of real-time applications on embedded devices. The performance of the proposed model on public datasets PASCAL VOC07 and PASCAL VOC12 is also better than that of other advanced lightweight algorithms, and the detection ability for occluded objects is significantly improved, which meets the requirements of mobile terminals for real-time detection in crowded indoor environments.

Peningkatan kualitas layanan perbankan digital melalui pengelompokan tweet menggunakan DBSCAN

Digitalization in the banking sector allows customers to obtain banking services independently without having to come directly to the bank. Digital banking services enable customers to obtain information, communicate, register, open accounts, banking transactions and close accounts, including obtaining other information and transactions outside of banking products. Banking is intensively providing services or promotions through social media, one of which is by using Twitter social media. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. DBSCAN clustering is done by combining the Eps and MinPts values to produce the highest silhouette coefficient. The highest silhouette coefficient values from BRI, Mandiri and BCA banking service tweets produce 33, 14, and 39 clusters, respectively, with different Eps and MinPts values. Based on the results of wordcloud, it shows that banking services need to be improved in terms of checking DM on accounts, customers ask the admin to immediately respond to complaints related to ATM cards, disruptions to mobile banking and some say thank you for the services that have been provided.

Multitarget Detection and Tracking by Mitigating Spot Jammer Attack in 77-GHz mm-Wave Radars: An Experimental Evaluation

Small form factor radar sensors at millimeter wavelengths find numerous applications in the industrial and automotive sectors. These radar sensors provide improved range resolution, good angular resolution, and enhanced Doppler resolution for short range and ultrashort ranges. However, it is challenging to detect and track the targets accurately when a radar is interfered by another radar. This article proposes an experimental evaluation of a 77-GHz IWR1642 radar sensor in the presence of a second 77-GHz AWR1642 radar sensor acting as a spot jammer. A real-time experiment is carried out by considering five different targets of various cross sections, such as a car, a larger size motorcycle, a smaller size motorcycle, a cyclist, and a pedestrian. The collected real-time data are processed by four different constant false alarm rate detectors, cell averaging (CA)-CFAR, ordered statistics (OS)-CFAR, greatest of CA (GOCA)-CFAR, and smallest of CA (SOCA)-CFAR. Following that, data from these detectors are fed into two different clustering algorithms (density-based spatial clustering of applications with noise (DBSCAN) and K-means), followed by the extended Kalman filter (EKF)-based tracker with global nearest neighbor (GNN) data association, which provide tracks of various targets with and without the presence of a jammer. Furthermore, four different metrics [tracks reported (TR), track segments (TSs), false tracks (FTs), and track loss (TL)] are used to evaluate the performance of various tracks generated for two clustering algorithms with four detection schemes. The experimental results show that the DBSCAN clustering algorithm outperforms the K-means clustering algorithm for many cases.

DOIDS: An Intrusion Detection Scheme Based on DBSCAN for Opportunistic Routing in Underwater Wireless Sensor Networks.

In Underwater Wireless Sensor Networks (UWSNs), data should be transmitted to data centers reliably and efficiently. However, due to the harsh channel conditions, reliable data transmission is a challenge for large-scale UWSNs. Thus, opportunistic routing (OR) protocols with high reliability, strong robustness, low end-to-end delay, and high energy efficiency are widely applied. However, OR in UWSNs is vulnerable to routing attacks. For example, sinkhole attack nodes can attract traffic from surrounding nodes by forging information such as the distance to the sink node. In order to reduce the negative impact of malicious nodes on data transmission, we propose an intrusion detection scheme (IDS) based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm for OR (DOIDS) in this paper. DOIDS is based on small-sample IDS and is suitable for UWSNs with sparse node deployment. In DOIDS, the local monitoring mechanism is adopted. Every node in the network running DOIDS can select the trusted next hop. Firstly, according to the behavior characteristics of common routing attack nodes and unreliable underwater acoustic channel characteristics, DOIDS selected the energy consumption, forwarding, and link quality information of candidate nodes as the detection feature values. Then, the collected feature information is used to detect potential abnormal nodes through the DBSCAN clustering algorithm. Finally, a decision function is defined according to the time decay function to reduce the false detection rate of DOIDS. It makes a final judgment on whether the potential abnormal node is malicious. The simulation results show that the algorithm can effectively improve the detection accuracy rate (3% to 15% for different scenarios) and reduce the false positive rate, respectively.

A novel density-based clustering method for effective removal of spurious intersections in bearings-only localization

In bearings-only localization, clustering-based methods have been widely used to remove spurious intersections by fusing multiple bearing measurements from different observation stations. Existing clustering methods, including fuzzy C-mean (FCM) clustering and density-based spatial clustering of applications with noise (DBSCAN), must specify the number of clusters and the threshold for defining the neighborhood density, respectively, which are always unknown and difficult to estimate. Moreover, in dense radiation source scenes, existing clustering methods for removal of spurious intersections all deteriorate significantly. Therefore, we propose a novel density-based clustering method called K-M-DBSCAN, which combines the minimum K distance algorithm with Mahalanobis distance-based DBSCAN clustering. Firstly, K-M-DBSCAN uses minimum K distance algorithm for preprocessing to remove most of the spurious intersections and reduce the computational complexity of clustering. Mahalanobis distance-based DBSCAN is used for clustering and spurious intersections recognition. In order to adapt the large variations of sample density in clustering, we use Mahalanobis distance to define an explicit neighborhood of DBSCAN instead of traditional Euclidean distance. Simulation results show that the proposed K-M-DBSCAN performs better than FCM and DBSCAN in removing of spurious intersections.

Global Seasonal-Scale Meteorological Droughts. Part I: Detection, Metrics, and Inland/Coastal Types

Knowledge of spatiotemporal characteristics and process evolutions is the fundamental basis of understanding drought mechanisms, especially from a global perspective. For a comprehensive investigation, we implemented event detection, type grouping, and spatiotemporal metrics from 3-dimensional (3D, longitude–latitude–time) perspectives. The major procedures and achievements were as follows. First, we identified global-scale seasonal-scale meteorological drought events following the recently proposed 3D DBSCAN (Density-Based Spatial Clustering of Applications with Noise)-based workflow of event detection. The 3D DBSCAN clustering algorithm can directly obtain arbitrarily shaped point collections over a given 3D space, as drought events can spread over space and evolve over time. Subsequently, these detected drought events are further grouped into inland and coastal types, as the observations revealed that some droughts over coastal regions originate from, extend to, or are accompanied by long-term precipitation deficits over adjacent oceans. Third, typical spatiotemporal characteristics (e.g., lifetime, genesis locations, migration/local developments, and process evolutions) were investigated with coastal/inland-type differences considered. The drought ratios originating from continents in all coastal-type droughts were ~50% over Africa, Asia, and South America, indicating the nonnegligible extension from continents to oceans. Additionally, process evolution-based analysis revealed intensity variations in intensification or recession phases, and coastal types overall displayed larger intensity variations than inland types. Moreover, ~92% of inland types and ~70% of coastal types can be treated as having symmetric development. Notably, the grouping type and spatiotemporal metrics herein can provide adequate preliminary knowledge for global-scale drought mechanism exploration.

Efficient and Fast Active Equalization Method for Retired Battery Pack Using Wide Voltage Range Bidirectional Converter and DBSCAN Clustering Algorithm

This article presents an efficient and fast echelon battery equalization method based on wide voltage range bidirectional converter combined with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DBSCAN (density-based spatial clustering of applications with noise)</i> clustering control strategy. Thanks to the wide voltage range and bidirectional buck-boost characteristics of the four-switch bidirectional converter, the battery energy can be reasonably redistributed between the battery and the super capacitor. The converter operating in synchronous rectification mode can not only achieve bidirectional energy flow in a wide voltage range, but also has a high energy conversion efficiency. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DBSCAN</i> clustering algorithm is used to divide all battery cells into groups, and each group may contain one or more adjacent and nonadjacent battery cells. First, the nonadjacent single cells are balanced and integrated into adjacent group with the closest voltage. Then, the groups with different voltages are balanced by group-to-group, thereby realizing rapid balance of the entire battery pack. In order to verify the effectiveness of the proposed method, simulations and experimental verification were carried out. The experimental results show that, comparing with traditional battery balancing methods, the proposed method achieves shorter balancing time and higher balancing efficiency.

Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

Fault diagnosis for cell voltage inconsistency of a battery pack in electric vehicles based on real-world driving data

Cell voltage inconsistency of a battery pack is the main problem of the Electric Vehicle (EV) battery system, which will affect the performance of the battery and the safe operation of electric vehicles. In real-world vehicle operation, accurate fault diagnosis and timely prediction are the key factors for EV. In this paper, real-world driving data is collected from twenty all-electric buses for many years and divided into three driving fragments to analyze cell voltage inconsistency and summarize the voltage characteristics of the cell when an inconsistency fault occurred. A fault diagnosis method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed for timely localization of the abnormal battery cell. It is found that the DBSCAN clustering algorithm has shown better effectiveness and accuracy as compared to K-means to locate irregular battery cells. A fault prediction method based on the Least-Square Support Vector Regression (LS-SVR) is developed to predict the change of the monomer voltage. The experimental comparison show that LS-SVR has better prediction accuracy than ordinary Support Vector Regression (SVR), and it can make short-term predictions based on the voltage difference and monomer voltage value for cell consistency failures and over/under voltage faults.