Calinski-Harabasz Index Research Articles

Analysis and modelling of a ML-based NIDS for IoT networks

As cyber-attacks become increasingly sophisticated, cybersecurity threats continue to increase. Therefore, it becomes a great challenge to detect, identify and prevent adversary attacks. The inability to detect and avert such attacks can break down the credibility of security services, e.g. data confidentiality, availability, and integrity. An intrusion detection system aims to detect, identify and respond to malicious activities involving computing and network resources. This is even more difficult when the network contains Internet of Things (IoT) devices. The process of developing an intrusion detection system is a tremendous challenge due to false alarm rates, low detection rates, unbalanced datasets and response time. This paper gives a comprehensive model of a network intrusion detection system (NIDS) by applying supervised and unsupervised machine learning (ML) over the NF-ToN-IoT-v2 dataset. Using supervised learning, implemented by Azure automated ML (AML), it is shown that the best results are achieved by algorithm XGBoostClassifier, obtaining an F-Score of 98.8%. When a custom made automated ML (AE2EML) is implemented, the best results are achieved by Random Forest Classifier, obtaining an F-Score of 98.6%. Using clustering with PCA (Principal Component Analysis), implemented by PyCaret automated ML, the proposed ML-based NIDS achieves a Silhouette score of 0.553, Calinski-Harabasz index of 1533106 and Davies-Bouldin index of 0.631.

Cluster analysis of time series K-Means line loss based on DWT distance

This paper constructs an indicator system composed of inherent attributes and time characteristics of the line based on the line loss, and proposes a K-Means line loss cluster analysis model based on this indicator system. The line is classified according to the clustering results. The result is 314.51 on the CH index (Calinski Harabasz Index), 0.19 on the Silhouette Cofficient (Silhouette Cofficient), and a running time of 0.508s. Compared with the traditional algorithm, it is greatly improved. The field of line loss analysis has guiding significance.

Unit Commitment under Uncertainty using Data-Driven Optimization with Clustering Techniques

This paper proposes a novel robust unit commitment (UC) framework with data-driven disjunctive uncertainty sets for volatile wind power outputs, assisted by machine learning techniques. To flexibly identify the uncertainty space based on wind power forecast error data with disjunctive structures, the uncertainty data are grouped using K-means and density-based spatial clustering of applications with noise following the optimal cluster number determined by the Calinski-Harabasz index. The disjunctive uncertainty sets are constructed accordingly as the union of multiple basic uncertainty sets, including conventional box and budget uncertainty sets, and data-driven uncertainty sets using Dirichlet process mixture model, principal component analysis coupled with kernel density estimation, and support vector clustering. The problem is formulated into a two-stage adaptive robust UC model with data-driven disjunctive uncertainty sets and with a multi-level optimization structure. To facilitate the solution process, a tailored decomposition-based optimization algorithm is developed. The effectiveness of the proposed framework is illustrated using an application on the IEEE 39-bus system. The proposed approach can reduce the price of robustness by 8-38 % compared to the conventional “one-set-fits-all” approaches. Benchmarking with stochastic programming indicates that the proposed framework can achieve the same or better economic performance with over 30 % less computational time.

The spatio-chronological distribution of Achnatherum splendens influences soil bacterial communities in degraded grasslands

Achnatherum splendens (Trinius) Nevskia dominates areas of degraded grasslands in China. However, the spatio-chronological patterns of A. splendens and its soil properties remain unknown. We conducted a field study comprising four tussock diameter classes (i.e., an age series) of A. splendens in three degraded grasslands of China (HB: Hebei Province; IM: Inner Mongolia, and QH: Qinghai Province) to determine the soil bacterial community and diversity in the root-associated soil of A. splendens by high-throughput sequencing technology. Achnatherum splendens exhibited strong adaptability and could survive under a low soil water content in the region of IM and in highly alkaline soil in the region of HB. Our findings demonstrated that the biomass of the degraded A. splendens grasslands had a significantly different spatio-chronological distribution, and the plant biomass decreased significantly as the diameter increased. The main bacteria in the root-associated soil of A. splendens showed a significant difference at the spatial scale. Based on the Calinski–Harabasz index, HB belonged to the Actinobacteria enterotype, and IM and QH belonged to the Proteobacteria enterotype, implying that the bacterial structure in root-associated soil of A. splendens had significant spatial differences that were mainly affected by climate factors. The relative abundances of Firmicutes and Gemmatimonadetes decreased significantly as the plant tussock diameter increased. The plant C/N ratio, plant biomass, and soil pH had a positive impact on bacterialβ-diversity between the three habitats, but negatively influenced the bacterial Shannon diversity (α-diversity) in the A. splendens root-associated soil. Our findings highlight that A. splendens not only reduces plant productivity as the diameter increased, but also changes the soil microbial characteristics. Therefore, the spatio-chronological preventive treatment of A. splendens, especially for the older age classes, is very important for improving the productivity of degraded grasslands in China.

Euclidean distance stratified random sampling based clustering model for big data mining

Big data mining is related to large-scale data analysis and faces computational cost-related challenges due to the exponential growth of digital technologies. Classical data mining algorithms suffer from computational deficiency, memory utilization, resource optimization, scale-up, and speed-up related challenges in big data mining. Sampling is one of the most effective data reduction techniques that reduces the computational cost, improves scalability and computational speed with high efficiency for any data mining algorithm in single and multiple machine execution environments. This study suggested a Euclidean distance-based stratum method for stratum creation and a stratified random sampling-based big data mining model using the K-Means clustering (SSK-Means) algorithm in a single machine execution environment. The performance of the SSK-Means algorithm has achieved better cluster quality, speed-up, scale-up, and memory utilization against the random sampling-based K-Means and classical K-Means algorithms using silhouette coefficient, Davies Bouldin index, Calinski Harabasz index, execution time, and speedup ratio internal measures.

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups.

ObjectiveDeep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate.Materials and MethodsEnabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively.ResultsCompared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes.DiscussionDICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps.ConclusionDICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

Clustering Methods for Power Quality Measurements in Virtual Power Plant

In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.

Retraction Note to: Motorized Level of Service Classification for Urban Uncontrolled Intersections

Service and total delay are considered for classifying MLOS and intersections, respectively. GPS is used to collect travel time and speed data for turning movements that are transformed into average delay values. Thirteen junctions from eight different cities in India form the dataset. Divisive followed by agglomerative clustering (DAC-HAC) algorithm is applied as a two-step process for obtaining the service and total delay ranges. Validation of clusters is performed based on the Davies-Bouldin score, Calinski-Harabasz index, and Silhouette gaps. Based on DAC-HAC, uncontrolled intersections are classified into six categories (Cat-I, II, III, IV, V, and VI). Results indicate MLOS classes “D”, “E” and “F” have significantly higher service delay ranges as compared to Highway capacity manual “control” delay ranges indicating mixed traffic conditions. Most of the uncontrolled intersections under mixed traffic fall under Cat-IV, V, and VI, having higher total delay ranges (> 60 s/ vehicle/ approach). Finally, validation of the clustering results is done for geometric and roadside environmental features.

Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale

Learning activities are one of the processes of delivering information or messages from teachers to students. SMPN 4 Sidoarjo is a State Junior High School (JHS) located in Sidoarjo Regency. During the learning process, the collected academic score data were still not well organized by teachers and school principals in monitoring student learning performance. The score data is from Bahasa Indonesia subject from a teacher with 222 data included at 2019/2020 school year. The method used in student clustering is K-Means. The number of clusters are determined using the elbow method and displayed in graphic form. Clustering result can be used as a reference for teachers in determining study groups and determining the best treatment for each cluster. The best clustering results are proven by validation score using Davies-Bouldin Index, Silhouette Width, and Calinski-Harabasz Index. Three clusters were obtained for each class level of data, while the cluster ranges from two to five for the data for each study group. The dashboard is used in order to visualize the clustering result. Usability testing using System Usability Scale (SUS) has a score value of 87.5, which means that the dashboard can be accepted by SMPN 4 Sidoarjo.

Comparative Analysis of Three Methods for HYSPLIT Atmospheric Trajectories Clustering

Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to obtain backward trajectories and then conduct clustering analysis is a common method to analyze potential sources and transmission paths of atmospheric particulate pollutants. Taking Qingdao (N36 E120) as an example, the global data assimilation system (GDAS 1°) of days from 2015 to 2018 provided by National Centers for Environmental Prediction (NCEP) is used to process the backward 72 h trajectory data of 3 arrival heights (10 m, 100 m, 500 m) through the HYSPLIT model with a data interval of 6 h (UTC 0:00, 6:00, 12:00, and 18:00 per day). Three common clustering methods of trajectory data, i.e., K-means, Hierarchical clustering (Hier), and Self-organizing maps (SOM), are used to conduct clustering analysis of trajectory data, and the results are compared with those of the HYSPLIT model released by National Oceanic and Atmospheric Administration (NOAA). Principal Component Analysis (PCA) is used to analyze the original trajectory data. The internal evaluation indexes of Davies–Bouldin Index (DBI), Silhouette Coefficient (SC), Calinski Harabasz Index (CH), and I index are used to quantitatively evaluate the three clustering algorithms. The results show that there is little information in the height data, and thus only two-dimensional plane data are used for clustering. From the results of clustering indexes, the clustering results of SOM and K-means are better than the Hier and HYSPLIT model. In addition, it is found that DBI and I index can help to select the number of clusters, of which DBI is preferred for cluster analysis.

A Novel Approach For Diagnosis Of Dermatologic Diseases Based On Multi-Objective Clustering Using Particle Swarm Optimization

The stratification of skin diseases plays a major role in enabling effective and customized medicine. An important task in the stratification of skin diseases is to discover subtypes of diseases for effective treatment. To achieve this goal, research into cluster algorithms for the stratification of skin diseases has attracted the attention of both scholars and the medical community in recent decades. However, cluster algorithms suffer from realistic limitations such as experimental sounds, high dimension and poor interpretation. In particular, cluster algorithms usually determine the quality of clusters with only one internal evaluation operation. Unfortunately, it is obvious that one internal evaluation operation is difficult to create and strong for all datasets. Therefore, this article proposes a novel of many descriptive frameworks called a multi objective cluster algorithm with a quick search and finding the density of peaks to deal with these limitations altogether. In the proposed framework, the variable component of the candidate is developed under many objectives to select properties and estimate the density of the cluster automatically. To guide the development of multi-objective, two cluster value indices are used, including the Calinski-Harabasz index and the Davies-Bouldin index. The clustering is performed with the help of a k-means cluster by finding the best clusters using particle optimization. PSO will solve the multi-objective to determine the best cluster. Here, the proposed method will test on the dermatology dataset from UCI repository using MATLAB R2020a, under windows10 environment. Then, the proposed method performance will be evaluating using the cluster evaluation indices.

School motivation profiles of Dutch 9th graders

The aim of this study was to identify school motivation profiles of Dutch 9th grade students in a four-dimensional motivation space, including mastery, performance, social and extrinsic motivation. Multiple clustering methods (K-means, K-medoids, restricted latent profile analysis) and multiple indices for selecting the optimal number of clusters were applied. The statistical selection methods did not completely concur on the optimal number of clusters, but a clear common denominator was provided by the Calinski-Harabasz index and the minimum and mean Silhouette values. All three indices indicated two clusters as the optimal number, regardless of the clustering method used: one cluster of 9th graders with high average scores on all dimensions and one cluster with low mean scores on all dimensions. In addition, we explored the substantive interpretation of multiple cluster solutions. It was discovered that most students are in clusters that can be classified into one of three profile types that may differ in level: (1) approximately equal mean scores on all dimensions, (2) relative high mean scores on mastery and social motivation, and (3) a relatively low mean score on performance motivation. The latter profile type is believed to be a new discovery.

PREDICTION OF FORMATION ENERGY USING TWO-STAGE MACHINE LEARNING BASED ON CLUSTERING

The formation energy (Hf) is one of the important properties associated with the thermodynamic stability of ABO3-type perovskite. In this work, two-stage machine learning based on hierarchical clustering and regression was designed for improving the prediction values of the density-functional theory (DFT) Hf of ABO3-type perovskites. A global dataset was clustered into Cluster 1 and Cluster 2 using the CHI (the Calinski-Harabasz index). To compare the prediction performances of Hf, DTR (decision tree regression), GBRT (gradient boosted regression trees), RFR (random forest regression) and ETR (extra tree regression) were applied to build models of Cluster 1, Cluster 2 and the global dataset, respectively. The results showed that all four different regression models of Cluster 1 had a higher R2, and lower MSE and MAE than those of the global dataset, while the models of Cluster 2 were poorer. Meanwhile, the GBRT model of Cluster 1 achieved a higher R2 of 0.917, and lower MSE and MAE of 0.033 eV/atom and 0.125 eV/atom. We further validated and compared the generalization ability of the models by predicting the Hf of ABO3-type perovskite previously unseen in the training set. The two-stage machine-learning models proposed here can provide useful guidance for accelerating the exploration of materials with desired properties.

An improved K-means clustering algorithm for global earthquake catalogs and earthquake magnitude prediction

The occurrences of earthquakes have no any explicit warning, and earthquake magnitude prediction is still extremely challenging now. Therefore, this study proposes a seismic prediction model based on clustering of global earthquake data. First, an improved K-means clustering algorithm for global earthquake catalogs is proposed. Traditional K-means clustering has several limitations, i.e., the number of clusters needs to be initialized, the initial cluster centers are arbitrarily selected, and there is currently no magnitude parameter in the K-means clustering algorithm. To improve the algorithm, this study employs the space–time–magnitude (STM) distance and then proposes a maximum–minimum STM distance for the selection of the initial cluster centers. Additionally, the sum of squares error, Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient are applied to determine the number of clusters. Subsequently, a seismic prediction model based on the clustering result combined with an artificial neural network is presented. Application of the improved clustering algorithm to the global seismic catalog from 1900–2019 obtained from the United States Geological Survey reveals better clustering accuracy than the traditional K-means algorithm and is also effective for seismic risk analysis in the local region. Furthermore, the seismic prediction model based on the clustering result also exhibits good performance, which has practical significance and reference value for future predictions of earthquake magnitude.

Automatic identification of the number of clusters in hierarchical clustering

Hierarchical clustering is one of the most suitable tools to discover the underlying true structure of a dataset in the case of unsupervised learning where the ground truth is unknown and classical machine learning classifiers are not suitable. In many real applications, it provides a perspective on inner data structure and is preferred to partitional methods. However, determining the resulting number of clusters in hierarchical clustering requires human expertise to deduce this from the dendrogram and this represents a major challenge in making a fully automatic system such as the ones required for decision support in Industry 4.0. This research proposes a general criterion to perform the cut of a dendrogram automatically, by comparing six original criteria based on the Calinski-Harabasz index. The performance of each criterion on 95 real-life dendrograms of different topologies is evaluated against the number of classes proposed by the experts and a winner criterion is determined. This research is framed in a bigger project to build an Intelligent Decision Support system to assess the performance of 3D printers based on sensor data in real-time, although the proposed criteria can be used in other real applications of hierarchical clustering.The methodology is applied to a real-life dataset from the 3D printers and the huge reduction in CPU time is also shown by comparing the CPU time before and after this modification of the entire clustering method. It also reduces the dependability on human-expert to provide the number of clusters by inspecting the dendrogram. Further, such a process allows applying hierarchical clustering in an automatic mode in real-life industrial applications and allows the continuous monitoring of real 3D printers in production, and helps in building an Intelligent Decision Support System to detect operational modes, anomalies, and other behavioral patterns.

An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns

Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.

An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches.

(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.

Clustering of graphs using pseudo-guided random walk

Clustering is an unsupervised learning task that models data as coherent groups. Multiple approaches have been proposed in the past to cluster large volumes of data. Graphs provide a logical mapping of many real-world datasets rich enough to reflect various peculiarities of numerous domains. Apart from k-means, k-medoid, and other well-known clustering algorithms, utilization of random walk-based approaches to cluster data is a prominent area of data mining research. Markov clustering algorithm and limited random walk-based clustering are the prominent techniques that utilize the concept of random walk. The main goal of this work is to address the task of clustering graphs using an efficient random walk-based method. A novel walk approach in a graph is presented here that determines the weight of the edges and the degree of the nodes. This information is utilized by the pseudo-guidance model to guide the random walk procedure. This work introduces the friends-of-friends concept during the random walk process so that the edges’ weights are determined utilizing an inclusive criterion. This concept enables a random walk to be initiated from the highest degree node. The random walk continues until the walking agent cannot find any unvisited neighbor(s). The agent walks to its neighbors if it finds a weight of one or more, otherwise the agent’s stopping criteria is met. The nodes visited in this walk form a cluster. Once a walk comes to halt, the visited nodes are removed from the original graph and the next walk starts in the remaining graph. This process continues until all nodes of the graph are traversed. The focus of this work remains random walk-based clustering of graphs. The proposed approach is evaluated using 18 real-world benchmark datasets utilizing six cluster validity indices, namely Davies-Bouldin index (DBI), Dunn index (DI), Silhouette coefficient (SC), Calinski-Harabasz index (CHI), modularity index, and normalized cut. This proposal is compared with seven closely related approaches from the same domain, namely, limited random walk, pairwise clustering, personalized page rank clustering, GAKH (genetic algorithm krill herd) graph clustering, mixing time of random walks, density-based clustering of large probabilistic graphs, and Walktrap. Experiments suggest better performance of this work based on the evaluation metrics.

GroupTracer: Automatic Attacker TTP Profile Extraction and Group Cluster in Internet of Things

As Advanced Persistent Threat (APT) becomes increasingly frequent around the world, security experts are starting to look at how to observe, predict, and mitigate the damage from APT attacks. In the meantime, the Internet of things devices are also risky and heavily exposed to the Internet, making them more easily used by hacker organizations to launch APT attacks. An excellent attacker can take down millions of Internet of things devices in a short time. Once the IoT botnet is built, attackers can use it to launch complex attacks which could damage Internet infrastructure and cause network disconnection. This paper proposes GroupTracer, a framework for observing and predicting the Internet of things attacks. GroupTracer is designed to automatically extract the TTP profiles (i.e., tactics, techniques, and procedures) that can describe the behavior of attackers through their tactics, techniques, and processes and dig out the potential attacker groups behind complex attacks. Firstly, it captures attacks by IoT honeypots and extracts relevant fields from logs. Then, attack behaviors are automatically mapped to the ATT&CK framework to achieve automatic TTP profiles extraction. After that, GroupTracer presents four feature groups, including TTP profiles, Time, IP, and URL features, a total of 18 features, mines potential attack groups through hierarchical clustering algorithm, and compares the clustering results with two baseline algorithms. As the ground truth labels are unknown, we apply three internal validation indexes to evaluate the cluster quantity. Experimental results showed that the proposed framework has achieved an excellent performance in exploiting potential groups as the Calinski–Harabasz index reaches 3416.93. Eventually, attack trees are generated for each cluster where nodes indicate attack commands and edges represent command sequences. These attack trees could help better understand each attack group’s actions and techniques.

RETRACTED ARTICLE: Motorized Level of Service Classification for Urban Uncontrolled Intersections

Service and total delay are considered for classifying MLOS and intersections, respectively. GPS is used to collect travel time and speed data for turning movements that are transformed into average delay values. Thirteen junctions from eight different cities in India form the dataset. Divisive followed by agglomerative clustering (DAC-HAC) algorithm is applied as a two-step process for obtaining the service and total delay ranges. Validation of clusters is performed based on the Davies-Bouldin score, Calinski-Harabasz index, and Silhouette gaps. Based on DAC-HAC, uncontrolled intersections are classified into six categories (Cat-I, II, III, IV, V, and VI). Results indicate MLOS classes “D”, “E” and “F” have significantly higher service delay ranges as compared to Highway capacity manual “control” delay ranges indicating mixed traffic conditions. Most of the uncontrolled intersections under mixed traffic fall under Cat-IV, V, and VI, having higher total delay ranges (> 60 s/ vehicle/ approach). Finally, validation of the clustering results is done for geometric and roadside environmental features.