Adaptive Density-based Spatial Clustering Research Articles

Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm for preventing MANET Cyber security attacks

ABSTRACT An Adaptive activation Functions with Deep Kronecker Neural Network optimized with Bear Smell Search Algorithm (BSSA) (ADKNN-BSSA-CSMANET) is proposed for preventing MANET Cyber security attacks. The mobile users are enrolled with Trusted Authority Using a Crypto Hash Signature (SHA-256). Every mobile user uploads their finger vein biometric, user ID, latitude and longitude for confirmation. The packet analyser checks if any attack patterns are identified. It is implemented using adaptive density-based spatial clustering (ADSC) that deems information from packet header. Geodesic filtering (GF) is used as a pre-processing method for eradicating the unsolicited content and filtering pertinent data. Group Teaching Algorithm (GTA)-based feature selection is utilized for ideal collection of features and Adaptive Activation Functions along Deep Kronecker Neural Network (ADKNN) is used to categorizing normal and attack packets (DoS, Probe, U2R, and R2L). Then BSSA is utilized for optimizing the weight parameters of ADKNN classifier for optimal classification. The proposed technique is executed in python and its efficiency is evaluated by several performances metrics, such as Accuracy, Attack Detection Rate, Detection Delay, Packet Delivery Ratio, Throughput, and Energy Consumption. The proposed technique provides 36.64%, 33.06%, and 33.98% lower Detection Delay on NSL-KDD dataset compared with the existing methods.

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.

Short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients

The hyperglycemic state of people with diabetes can lead to metabolic and healthy disturbances in the body. Diabetes is mainly treated clinically by conservative treatment, which requires frequent and continuous measurement of blood glucose concentration. Accurate blood glucose prediction plays an important role in the future blood glucose management of patients. To improve the accuracy of blood glucose prediction, this paper proposes a short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients (PBGTAM). Firstly, a detection algorithm of abnormal blood glucose based on Adaptive Density-Based Spatial Clustering of Applications with Noise is proposed by using an autonomous neighborhood parameter selection method. Secondly, the imputation algorithm based on feature engineering is proposed to fill the missing blood glucose values. Thirdly, we propose temporal multi-head attention model to obtain global and local spatiotemporal features from sequence data, in which a temporal series features module is designed to insight the detail feature so as to keep the useful information contained in the details, and an intensity correlation module is used to obtain global features of sequence data and then to achieve strong correlation between blood glucose data, and a sequence features module is employed to learn temporal sequence features between sequence data to guarantee continuous prediction of future blood glucose values. Finally, the comparative results show that PBGTAM outperforms six state-of-the-art methods in terms of the overall prediction root mean square error, forecasting accuracy for warning, and clinical consistency with 20.57, 84.35% and 85.18%, respectively.

Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: <i>epsilon</i> (fixed neighborhood radius) and <i>MinPts</i> (the lowest number of objects in <i>epsilon</i>). However, it can’t handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one. Only user input in the proposed method is the <i>MinPts</i>. <i>Epsilon</i> on the other hand, is computed automatically based on statistical information of the dataset. The proposed method finds the core distance for each object in the dataset, takes the average of these distances as the first value of <i>epsilon</i>, and finds the clusters satisfying this density level. The remaining unclustered objects will be clustered using a new value of <i>epsilon</i> that equals the average core distances of unclustered objects. This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size. The proposed method requires <i>MinPts</i> only as an input parameter because <i>epsilon</i> is computed from data. Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results. Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them. The accuracy of the method ranges from 92% to 100% for the experimented datasets.

Improved Filtering of ICESat-2 Lidar Data for Nearshore Bathymetry Estimation Using Sentinel-2 Imagery

The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future.

Data mining approach for automatic ship-route design for coastal seas using AIS trajectory clustering analysis

In this paper, we propose an automatic route design method based on simple recurrent unit (SRU) and automatic identification system (AIS) data. Laplacian eigen maps and Gaussian kernel functions are used to compress the AIS data and extract the turning points of all ships. Fuzzy adaptive density-based spatial clustering of applications with noise (FA-DBSCAN) technique is used to cluster the turning points obtained at the preprocessing stage to obtain the turning region. Optimal turn region matching is used to connect the turning regions of similar routes, and the SRU neural network algorithm is used to learn the relationship between different types, sizes, and drafts of ships in each turning region; extract the feature-turning points; and obtain the recommended coastal routes, speed, and course of each type of ship. In the experimental stage, a large variety of AIS data from two sea areas are used to compare and analyze the designed route and real-ship data through LSTM and SRU experiments. The results show that the SRU algorithm improves the training speed and accuracy in comparison to LSTM, while the generated automatic route meets the requirements of navigation practice.

A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm

The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering.

Adaptive Density-Based Spatial Clustering for Massive Data Analysis

Clustering is a classical research field due to its broad applications in data mining such as emotion detection, event extraction and topic discovery. It aims to discover intrinsic patterns which can be formed as clusters from a collection of data. Significant progress have been made by the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and its variants. However, there is a major limitation that current density-based algorithms suffer from linear connection problem, where they perform poorly to discriminate objective clusters which are “connected” by a few data points. Moreover, the parameter setting and the time cost make it hard to be well-adapted in massive data analysis. To address these problems, we propose a novel adaptive density-based spatial clustering algorithm called Ada-DBSCAN, which consists of a data block splitter and a data block merger, coordinated by local clustering and global clustering. We conduct extensive experiments on both artificial and real-world datasets to evaluate the effectiveness of Ada-DBSCAN. Experimental results show that our algorithm evidently outperforms several strong baselines in both clustering accuracy and human evaluation. Besides, Ada-DBSCAN shows significant improvement of efficiency compared with DBSCAN.

Meta-Heuristic Optimization Based Two-stage Residential Load Pattern Clustering Approach Considering Intracluster Compactness and Inter-cluster Separation

This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.

Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise

We propose an unsupervised segmentation method based on simple non-iterative clustering (SNIC) and adaptive density-based spatial clustering of applications with noise (DBSCAN). The method is not sensitive to parameter settings. And cluster parameter suitable for each image can be automatically calculated. SNIC superpixel segmentation is applied in achieving over-segmented images to solve the problem of the image resolution being too high. Then, adaptive DBSCAN clustering is proposed to cluster the over-segmented superpixel blocks to solve the problem of over-segmentation and manual adjustment of DBSCAN parameters. Finally, k-means and connected regions are used for postprocessing to remove the shadow superpixel blocks from the clustered image and to ensure the integrity of a single microstructure. The effectiveness of this method is proved by many experiments. Based on this method, we provide a fast labeling method to help experts quickly label metallographic images.

Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.

An improved density-based spatial clustering of application with noise

Although the density-based spatial clustering of application with noise algorithm can identify clusters with arbitrary shape, there is a problem that the global parameter Eps needs to be manually set. In this paper, we propose a parameter adaptive density-based spatial clustering of application with noise by using the cuckoo search algorithm, which could solve the global optimization problem quickly. According to the cuckoo search algorithm to calculate the optimal global parameter Eps, the improved algorithm avoids human intervention in the process of clustering, and achieves clustering process automation. The simulation results show that the proposed algorithm in this paper can select the reasonable Eps parameter value and get the clustering results with high accuracy.

A Baseline Load Estimation Approach for Residential Customer based on Load Pattern Clustering

Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a residential CBL estimation approach based on load pattern (LP) clustering to improve the accuracy of CBL estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment residential customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of non-participants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods.

A MapReduce-based improvement algorithm for DBSCAN

This paper proposes an improved adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm based on genetic algorithm and MapReduce parallel computing programming frame...