Two-step Clustering Method Research Articles

Two-Step Clustering for Mineral Prospectivity Mapping: A Case Study from the Northeastern Edge of the Jiaolai Basin, China

The advancement of geological big data has rendered data-driven methodologies increasingly vital in Mineral Prospectivity Mapping. The effective integration of quantitative and qualitative data, including experiential and knowledge-based insights, is crucial in geological data fusion. Specifically, the conversion of raw data into samples and the selection of predictive methods are two core issues that constitute the focus of this study. Traditional clustering methods require the user to specify the number of clusters in advance. The two-step clustering can automatically determine the clustering result ‘k’ while analyzing both continuous and categorical variables, by building a Cluster Feature (CF) and using information criteria to merge nodes. In this study, we conducted an analysis utilizing stream sediment element data, residual gravity anomalies, and fault distribution through the two-step clustering method. Factor analysis (FA) was employed to reduce 16 elemental variables from stream sediments into five uncorrelated continuous variables; additionally, residual gravity anomalies were transformed from continuous to categorical variables via an interval-based method before being combined with fault distribution, resulting in seven variables for clustering. The research findings indicate that categorical variables significantly influence clustering results; concurrently, as the importance of continuous variables within the cluster increases, so does k. When only one categorical variable is present, residual gravity anomalies show significantly better clustering than fault distribution; however, when two categorical variables are involved, it is essential to consider the quantity of categories: more categories lead to poorer quality. The results from the Jiaolai Basin’s northeastern margin indicate a significant correlation with known gold deposits; two-step clustering is a promising and effective method for improving mineral prospecting efforts.

SEGMENTATION OF YOUNG ADULT TOURISTS VISITING CROATIAN CITIES: A HUNGARIAN CASE STUDY

Croatia's allure as a cultural, natural, and historical destination in the global tourism landscape has garnered increasing interest from international travelers, including Hungarian tourists. This study addresses the research gap in demographic segmentation and destination preference among Hungarian adults aged 18-34, a crucial yet understudied market segment within Croatian tourism. Our analysis integrates descriptive statistics and ordinal regression to assess how tourism preferences influence satisfaction. A two-step clustering method identifies demographic clusters, further analyzed to determine their visitation patterns to 11 Croatian cities, revealing significant travel behaviour insights. The findings reveal distinctive clusters with varied travel behaviours and preferences, indicating that tailored marketing strategies could significantly enhance the tourism experience. The travel patterns suggest a move towards personalized travel experiences and an inclination towards autonomy and comfort in accommodation choices. The study enriches the discourse in tourism segmentation and destination analysis, suggesting that understanding the intricate preferences of demographic segments can lead to more effective marketing approaches. Practically, it offers actionable insights for tourism operators, marketers, and policymakers to fine-tune their strategies to cater to this demographic, potentially leading to increased visitation and deeper engagement with Croatian tourism offerings.

Sustainable agricultural technologies of the future: Determination of adoption readiness for different consumer groups

The rapid increase in population and unsustainable agricultural practices has significantly damaged the environment. Our study explores how to achieve food security through environmentally friendly methods such as controlled environment agriculture (CEA), genetic editing (GE), and farming automation (FA), with an emphasis on the importance of consumer acceptance of these technologies. Analyzing data from 2138 Australians and 1760 New Zealanders, we employ a two-step clustering method—(1) hierarchical clustering and (2) k-means clustering—to categorize consumers on the basis of their demographic and lifestyle preferences into three segments that capture differences in attitudes toward new agricultural technologies: “Green Urbanites,” “Environmentally Unconcerned,” and “Skeptical Foodie.” Our findings reveal that Green Urbanites are the most open to adopting CEA, GE, and FA, while Skeptical Foodies and Environmentally Unconcerned are more reluctant. We contribute to theory by studying a novel facet of consumer acceptance of sustainable technologies and revealing how consumption, living, and lifestyle patterns motivate new technology acceptance in the agricultural sector. To inform practice, we suggest tailored strategies to increase engagement of the identified segments and promote wider acceptance of sustainable agricultural practices for enhanced food security.

Could free-floating bikeshare weed out station-based bikeshare? Analyzing the relationship between two bikeshare systems from bivariate flow clustering

Against the backdrop of the strong market expansion of free-floating bikeshare systems (FFBS), the future of government-funded station-based bikeshare system (SBBS) is a matter of controversy. Merely relying on point density analysis proves to be inadequate in reflecting the flow characteristic, this paper employs a flow clustering analysis to investigate the relationship between SBBS and FFBS. To recognize both bikeshare flow clusters with inhomogeneous density and shape in less time, we propose a two-step network-constrained bivariate flow clustering method that organically combines multiplex-network community detection and bivariate flow clustering method. The performance and applicability of the method in flow clustering detection is exemplified by the SBBS and FFBS systems in Nanjing. The results indicate that though FFBS outnumbers SBBS in terms of bikes, there are still specific flow clusters where SBBS performs better. Spatiotemporal patterns of flow clusters reveal that about one-third of flow clusters dominated by FFBS move from the metro station to the business district during the morning peak, while more SBBS-dominated flow clusters (55.4%) move from the residence to the metro station. Conversely, during the evening peak, flow clusters are observed in the opposite direction. Nevertheless, if the difference in bike numbers is significant, SBBS will be at a disadvantage. It is necessary for SBBS to prioritize resource allocation towards target groups and advantageous areas to prevent decentralized development. Our findings contribute to a more profound comprehension of the interplay between SBBS and FFBS, thereby offering more informed recommendations for strategically aligning the functions of bikeshare systems.

Applying a two-step cluster algorithm in traffic accident data analysis

Cluster analysis is often employed as the initial stage in organizing heterogeneous data into homogeneous groups. Choosing an effective clustering approach and an ideal number of clusters in a traffic accident dataset might be complex and challenging. This study aims to evaluate the effectiveness of k-means and two-step cluster methods. Subsequently, the two-step cluster method and GIS are applied to analyze the traffic accident datasets from 2015 to 2017 in Hanoi, Vietnam. First, according to the Silhouette score, the two-step cluster method achieved a higher score of 0.563, while the k-means method scored 0.341. A higher Silhouette score indicates more well-defined clusters. Second, the research suggests combining the two-step cluster method with GIS for analyzing traffic accident datasets. The outcome identifies five typical types of accidents in Hanoi. In addition, the locations of various accident types were visually illustrated on a map, enabling traffic officials to recommend precise and urgent countermeasures. Importantly, the clustering results reveal that the two-step cluster method exhibits a significantly higher rate of homogeneous data in the clusters compared to the k-means method. This study demonstrates that the two-step cluster method is not only more effective than the k-means method in terms of clustering ability but also in data pre-processing. The study's results enable authorities to gain a more detailed understanding of typical traffic accident patterns in Hanoi. Besides, the employed methods could potentially be applied to other regions, providing an additional avenue for analysis

Identification of the mining accidents by a two-step clustering method for the mining-induced seismicity

Clustering methods aim to categorize data or samples into distinct groups based on their similarity. When applying clustering methods to earthquake events, it is crucial to establish a metric for quantifying the similarity between these events. Directly applying this clustering method to a catalog of mining-induced seismicity may lead to clustering earthquake events induced by different mining activities or accidents into the same group. To address this issue, a two-step clustering method has been proposed and applied for analyzing a catalog of mining-induced seismicity. The first step involves spatial distance-based clustering of seismic events, while the second step focuses on moment tensor analysis-based clustering of these events. The results obtained from the MT-based clustering method are visualized using Hudson Graphs, and box plots serve as an evaluation tool for assessing the quality of MT clustering. Most box plots demonstrate desirable quality in terms of MT cluster results, indicating successful outcomes. By the proposed two-step clustering method combined with actual mining activities, the potential accident locations and categories can be hypothesized while valuable recommendations provided for mining operations.

Mapping 3-D classroom seats based on partial object point cloud completion

ABSTRACT 3-D classroom seating information is of great significance in a host of planning contexts including indoor navigation and facility management. Mapping classroom 3-D seats based on a single-view LiDAR scan is challenging because of occlusions and viewpoint limitations. In essence, one is left with incomplete point clouds, making object recognition difficult. The aim of this paper is to acquire complete point clouds for objects in a room based on a single-view LiDAR scan, focusing in particular on chairs in indoor spaces such as classrooms, lecture halls, theaters, concert venues, and sports arenas. A spatial optimization enabled framework is proposed for 3-D classroom seat mapping from a single-view LiDAR scan. A two-step clustering method is proposed to segment each individual chair object in the LiDAR scan. A chair model is extracted and used to complete other chair point clouds via registration. Notably, a parameter estimation method is devised based on spatial priors and integrated in an iterative closest point optimization approach to refine registration. Applications to multiple classroom scans demonstrate the effectiveness and accuracy of the proposed methodology. The developed segmentation-registration framework harnesses spatial knowledge to complete a single-view LiDAR scan with many identical objects. The resultant object point cloud is critical for navigation, route guidance, facility management, pandemic response and planning, indoor mapping and venue utilization, among others, which require precise and complete spatial information.

Sleep traits and associated factors among people living with HIV/AIDS in Iran: a two-step clustering analysis

Sleep plays an essential role in improving the quality of life of people living with HIV (PLWH); however, sleep traits in this population are not well studied. This study aims to evaluate the sleep traits and related associated factors among PLWH in Iran. A nationwide cross-sectional study was conducted with 1185 PLWH who attended Voluntary Counseling and Testing centers in 15 provinces in Iran between April 2021 and March 2022. The Berlin Obstructive Sleep Apnea questionnaire, Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and Insomnia Severity Index were used. A two-step clustering method was employed to identify the number of sleep clusters in PLWH. Prevalence of poor sleep quality, sleepiness and insomnia were 49.6%, 21.15% and 42.7% respectively. Three sleep trait clusters were identified: I. minor sleep problems (45.6%); II. Snoring & sleep apnea (27.8%), and III. poor sleep quality and insomnia (26.7%). Age (Odds Ratio (OR) 1.033, 95% Confidence Interval (CI) 1.017–1.050), academic education (OR 0.542, 95% CI 0.294–0.998) and HIV duration were associated with being in Snoring & sleep apnea cluster, while age (OR = 1.027, 95% CI 1.009–1.040) was associated with being in Poor sleep quality and insomnia cluster. PLWH with depression had higher odds of being in Poor sleep quality and insomnia cluster, and those with anxiety had higher odds of being in Snoring & sleep apnea cluster and Poor sleep quality and insomnia cluster. A significant proportion of PLWH have poor sleep quality, sleepiness, and insomnia. The identification of three distinct sleep trait clusters underscores the need for increased attention and tailored interventions to address the specific sleep issues experienced by PLWH.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: long-term deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslide-prone areas.

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

EXPLORING SOME SPATIALLY CONSTRAINED DELINEATION METHODS IN SEGMENTING THE MALAYSIAN COMMERCIAL PROPERTY MARKET

This study delves into the property submarket in Kuala Lumpur and Selangor, Malaysia. The submarket is anticipated to be simple, uniform, and dense, making it highly influenced by neighbouring properties. However, traditional data-driven methods that overlook spatial contiguity disregard this density condition. To tackle this problem, the study investigates spatially constrained data-driven methods utilizing Principal Component Analysis (PCA) and cluster analysis. The findings reveal that spatially constrained methods outperform traditional methods by minimizing errors and enhancing model fit. Specifically, the two-step cluster method and k-means cluster method reduce errors by 6.96% and 7.22%, respectively, but at the cost of model fit by 11.23% and 13.94%. Conversely, the spatial k-means and spatial agglomerative hierarchical cluster methods reduce errors by 8.68% and 8.17%, respectively, while improving model fit by 7.1% and 6.35%. Hence, the study concludes that spatially constrained data-driven methods are more effective in differentiating commercial property submarkets than traditional methods.

Exploring the plot patterns of the retail landscape: The case of the Helsinki Metropolitan area

This study examines the impact of the morphological characteristics of plots in the Helsinki Metropolitan Area (HMA) on the retail landscape, with a focus on understanding the ways in which the morphological characteristics potentially influence the distribution, agglomeration and diversity of retail businesses. Although frequently underestimated in contemporary placemaking practices, this research emphasizes the role of the dual nature of plots as an element of urban form and an element of control over the retail landscape of the city. In this sense, the role of the morphological characteristics of plots in shaping the retail landscape of the city was investigated. The compositional (size, frontage ratio) and configurational (integration, betweenness, frequency) features of the plot in the HMA ( n = 77,736) were measured. Thereafter an unsupervised two-step clustering method was applied to reveal the subtle morphological regions through plot patterns. Computational plot characterization with open data sets yielded six plot types having different morphological characteristics and geographic distribution patterns. The spatial capacities of each plot type for retail distribution, agglomeration and diversity were then analysed and compared. This research argues that the interrelationship of the dual nature of plot plays an important role in placemaking processes. The results suggest that the spatial capacity of plots to accommodate street-based retail clusters is improved with spatially integrated, fine-grained urban fabric with independent micro-businesses involving a diversity of uses and actors. The study argues that these spatial conditions might also increase retail resilience and contribute to the vitality and viability of the retail landscape.

A statistical analysis of work-related accidents in women’s employment

Women are often victims of gender discrimination in various fields, including in the work environment. This research article aims to statistically evaluate the status of women’s employment and occupational accidents in some countries where regular data are available, and to provide predictions in the future. Using quantitative methods, data were analyzed with panel data analysis, bivariate correlation and two-step clustering methods. The findings of this study indicate that there is a positive and stronger correlation between work-injury for women than for men in all but a few developed social welfare countries. In some countries, while the correlation is negative for men, it is positive and strong for women. This study concludes that existing preventive regulations, including those in developed countries, are not sufficient to protect women. Women are more likely to work in non-traditional sectors where occupational health and safety (OHS) regulations are more flexible. Therefore, public authorities related to OHS should focus more on the conditions of this new industry in the future.

Changes in the Culture of Consumption During COVID-19: a Decision-Tree Model

This study aimed to understand changes in the culture of consumption in the selected consumer seg-ments regarding COVID-19 during the lockdown. The empirical research was conducted with a sample of 534 households. To predict changes in consumer behavior, the two-step clustering method was used. We also use the decision-tree model. We created four selected segments with remarkable consumption changes on the basis of their opinions concerning three points of view, namely, behavior changes, de-mographic data, and self-beliefs. Consequently, we classified subject pertinence (i.e., single respon-dents) to single clusters by decision trees using various statistical calculations to place the chosen attri-bute within the node of the tree structure. The factors most statistically significant for inclusion in the cluster were willingness to give up personal freedom, conservatism, and preferring functionality to appearance/design, with the last significant factor being meeting deadlines. We find indications that the more people purchase online, the less they decrease expenses and support charities, or/and the more people solve things remotely, the less they restrict taking loans. The results will help in identifying future models in retail conditions.

Identification of Unknown Abnormal Conditions of Absorption Stabilization System in Catalytic Cracking Process Based on Cyclic Two-Step Clustering Analysis and Convolutional Neural Network

Machine learning for online monitoring of abnormalities in fluid catalytic cracking process (FCC) operations is crucial to the efficient processing of petroleum resources. A novel identification method is proposed in this paper to solve this problem, which combines cyclic two-step clustering analysis with a convolutional neural network (CTSC-CNN). Firstly, through correlation analysis and transfer entropy analysis, key variables are effectively selected. Then, the clustering results of abnormal conditions are subdivided by a cyclic two-step clustering (CTSC) method with excellent clustering performance. A convolutional neural network (CNN) is used to effectively identify the types of abnormal operating conditions, and the identification results are stored in the sample database. With this method, the unknown abnormal operating conditions before can be identified in time. The application of the CTSC-CNN method to the absorption stabilization system in the catalytic cracking process shows that this method has a high ability to identify abnormal operating conditions. Its use plays an important role in ensuring the safety of the actual industrial production process and reducing safety risks.

Robust Discriminant Subspace Clustering With Adaptive Local Structure Embedding.

Unsupervised dimension reduction and clustering are frequently used as two separate steps to conduct clustering tasks in subspace. However, the two-step clustering methods may not necessarily reflect the cluster structure in the subspace. In addition, the existing subspace clustering methods do not consider the relationship between the low-dimensional representation and local structure in the input space. To address the above issues, we propose a robust discriminant subspace (RDS) clustering model with adaptive local structure embedding. Specifically, unlike the existing methods which incorporate dimension reduction and clustering via regularizer, thereby introducing extra parameters, RDS first integrates them into a unified matrix factorization (MF) model through theoretical proof. Furthermore, a similarity graph is constructed to learn the local structure. A constraint is imposed on the graph to guarantee that it has the same connected components with low-dimensional representation. In this spirit, the similarity graph serves as a tradeoff that adaptively balances the learning process between the low-dimensional space and the original space. Finally, RDS adopts the l2,1 -norm to measure the residual error, which enhances the robustness to noise. Using the property of the l2,1 -norm, RDS can be optimized efficiently without introducing more penalty terms. Experimental results on real-world benchmark datasets show that RDS can provide more interpretable clustering results and also outperform other state-of-the-art alternatives.

Study on Grouping Model of Diseases in Traditional Chinese Medicine Hospital Based on Two-Step Clustering Method

The purpose of this study is to explore the applicability of the current CHS-DRG grouping and payment scheme in Traditional Chinese Medicine (TCM) hospitals by grouping the discharged cases in TCM hospitals. It also aims to provide a method reference for discussing the related grouping of TCM disease and syndrome diagnosis. The first-page data of inpatient medical records in some TCM hospitals above the county level, from 2015 to 2020, were taken as the research object. The first-page data of inpatient medical records from 2015 to 2017 were grouped according to the CHS-DRG grouping rules, and the grouping results were obtained. Subsequently, two-stage clustering methodology was used to further group the bad groups, and the TCM disease grouping model was preliminarily formed. The model was verified by the first-page data of inpatient medical records from 2018 to 2020. The results showed that under the CHS-DRG grouping model, 494,150 cases were divided into 359 DRG groups. Only 92 cases with CV < 1 and cases > 100 were grouped poorly. After adding TCM diagnostic codes into the grouping model, the grouping effect of the disease group with CV > 1 was significantly increased, and the grouping results met the CHS-DRG grouping criteria. To fully leverage the benefits of Traditional Chinese Medicine and provide simple, convenient, empirical, and inexpensive medical services, TCM hospitals can explore TCM case grouping models that conform to the characteristics of TCM. In case grouping, TCM characteristic diagnosis and treatment elements should be included, and the study of medical insurance payment mode suitable for TCM hospitals should be conducted. Additionally, improving the hospital hardware facilities and providing an intelligent, automatic medical record management system should be prioritized. Further standardizing TCM diagnosis and code filling in the medical record information system and strengthening the study of TCM medical record management and coding will improve the quality of medical record.

Identifying Working Trajectories of the Wheat Harvester In-Field Based on K-Means Algorithm

Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the “turning”, “working”, and “abnormal working” trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the “turning” and “abnormal working” trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing.

Large truck fatal crash severity segmentation and analysis incorporating all parties involved: A Bayesian network approach

Studies on fatal crashes involving large trucks are typically focused on the examination of injury severity at the personal level rather than the crash level. In this study, instead of adopting the predefined, commonly used rule of transformation of individual injury severity to crash level severity, we develop a data specific transformation approach. Crash-level severity for fatal large-truck involved crashes is first identified applying a two-step clustering method. A Bayesian network is then constructed to investigate the impacts of influential factors on the crash severity. Data obtained from the Fatality Analysis Reporting System in 2019, are used to calibrate the model. The results indicate that the number of vehicles involved in the crash, collision manner, truck-airbag deployment, overturn of vehicles, driver’s speeding offense, and gross vehicle weight are the key determinants of crash injury severity. Uni- and two-dimensional inference analyses were conducted to determine the relations among these factors and their association with crash severity.

Implementation of a Clustering-Based LDDoS Detection Method

With the rapid advancement and transformation of technology, information and communication technologies (ICT), in particular, have attracted everyone’s attention. The attackers took advantage of this and can caused serious problems, such as malware attack, ransomware, SQL injection attack, etc. One of the dominant attacks, known as distributed denial-of-service (DDoS), has been observed as the main reason for information hacking. In this paper, we have proposed a secure technique, called the low-rate distributed denial-of-service (LDDoS) technique, to measure attack penetration and secure communication flow. A two-step clustering method was adopted, where the network traffic was controlled by using the characteristics of TCP traffic with discrete sense; then, the suspicious cluster with the abnormal analysis was detected. This method has proven to be reliable and efficient for LDDoS attacks detection, based on the NS-2 simulator, compared to the exponentially weighted moving average (EWMA) technique, which has comparatively very high false-positive rates. Analyzing abnormal test pieces helps us reduce the false positives. The proposed methodology was implemented using Python for scripting and NS-2 simulator for topology, two public trademark datasets, i.e., Web of Information for Development (WIDE) and Lawrence Berkley National Laboratory (LBNL), were selected for experiments. The experiments were analyzed, and the results evaluated using Wireshark. The proposed LDDoS approach achieved good results, compared to the previous techniques.