Clustering Framework Research Articles

Development of a compact Ansatz via operator commutativity screening: Digital quantum simulation of molecular systems.

Recent advancements in quantum information and quantum technology have stimulated a good deal of interest in the development of quantum algorithms toward the determination of the energetics and properties of many-fermionic systems. While the variational quantum eigensolver is the most optimal algorithm in the noisy intermediate scale quantum era, it is imperative to develop compact Ansätze with low-depth quantum circuits that are physically realizable in quantum devices. Within the unitary coupled cluster framework, we develop a disentangled Ansatz construction protocol that can dynamically tailor an optimal Ansatz using the one- and two-body cluster operators and a selection of rank-two scatterers. The construction of the Ansatz may potentially be performed in parallel over multiple quantum processors through energy sorting and operator commutativity prescreening. With a significant reduction in the circuit depth toward the simulation of molecular strong correlation, our dynamic Ansatz construction protocol is shown to be highly accurate and resilient to the noisy circumstances of the near-term quantum hardware.

Sales forecasting of a food and beverage company using deep clustering frameworks

The competition among Food & Beverage companies has substantially increased in today's age of digitization. Sales forecasting is one of their main challenges. Due to space limitations, employee shortages, and rising online demand, retail sales forecasting became extremely important for Food and Beverage companies. This research analyzed the sales data of a multinational Food & Beverage Company. It proposed a framework using Gaussian Mixture Model (GMM) clustering, Hierarchical Agglomerative Clustering (HAC), and Random Forest algorithm for forecasting sales. This model analyzes the impact of the weekends, holidays, promotional activities, customer sentiments, festivals, and socio-economic situations in sales data and is able to forecast sales ranging from one to 15 months. An investigation of the suggested model's performance compared to numerous cutting-edge sales forecasting techniques is carried out to show its efficacy. Here, we demonstrate that the proposed hybrid model surpasses current predicting and computing efficiency methods. The results of this study can help retail managers to allocate resources and manage inventories in well-informed ways. The findings suggest that combining many strategies may produce the most precise forecasts.

PROFESSIONAL STANDARDS FOR EDUCATIONAL MANAGERS: ANALISING THE EXPERIENCE OF COUNTRIES WITH DIFFERENT SOCIO-ECONOMIC DEVELOPMENT WITH THE VIEW OF ITS POSSIBLE USE IN UKRAINE

Professional standards for educational managers establish the compliance of a person with the professional requirements; therefore, it is important to study them in order to develop the content of university training programs for school principals. The aim of the present research is to examine the experience of standardization of educational managers’ professional activity in the countries of different socio-economic development and various degrees of participation in the global educational integration processes, as well as to highlight the possible use of best practices to develop and implement the most effective university training programs for educational managers. We conducted the study during 2018 – 2020 according to the methodology of the aspect analysis, having distinguished two aspects: functional and behavioural. We analysed the professional standards within the framework of regional clusters taking as a basis the mutual convergence and interaction of their educational systems, namely regions that are the generators of integration processes; regions that respond positively to integration processes but do not initiate them; regions that are inert to the integration of educational processes. Furthermore, we chose a few countries from each cluster, which adopted the standards at the state level or at the level of individual administrative units within a particular country. The results of the analysis made it possible to identify general and special trends, peculiar to certain groups of countries. The results obtained within the framework of the study made it possible for us to develop the construct of the standard that takes into account the particular features of socio-economic development of Ukraine.

Survey Paper: Optimization and Monitoring of Kubernetes Cluster using Various Approaches

This research compares different methods for optimizing and monitoring Kubernetes clusters. Three referenced journals are analyzed: "Kubernetes cluster optimization using hybrid shared-state scheduling framework" by Oana-Mihaela Ungureanu, Călin Vlădeanu, Robert Kooij; "Monitoring Kubernetes Clusters Using Prometheus and Grafana" by Salma Rachman Dira, Muhammad Arif Fadhly Ridha; and "Cluster Frameworks for Efficient Scheduling and Resource Allocation in Data Center Networks: A Survey" by Kun Wang, Qihua Zhou, Song Guo, and Jiangtao Luo. These journals explore various approaches to optimizing and monitoring Kubernetes clusters. This review concludes that selecting appropriate technologies for optimizing and monitoring Kubernetes clusters can enhance performance and resource management efficiency in data centre networks. The research addresses the problem of improving Kubernetes cluster performance through optimization and efficient monitoring. The required methods include utilizing hybrid state-sharing scheduling frameworks, implementing Prometheus and Grafana for monitoring, and employing efficient cluster frameworks. The study's findings demonstrate that adopting a hybrid shared-state scheduling framework can improve Kubernetes cluster performance. Additionally, leveraging Prometheus and Grafana as monitoring tools offer valuable insights into cluster health and performance. The survey also reveals various cluster frameworks that enable efficient scheduling and resource allocation in data centre networks. In conclusion, this research emphasizes the significance of employing suitable technologies to optimize and monitor Kubernetes clusters, leading to enhanced performance and efficient resource management in data centre networks. By leveraging appropriate scheduling frameworks and monitoring tools, organizations can optimize their utilization of Kubernetes clusters and ensure efficient resource allocation

Three-Stage Stackelberg Game Enabled Clustered Federated Learning in Heterogeneous UAV Swarms

In the past decade, the unmanned aerial vehicles (UAVs) swarm has become the disruptive force reshaping our lives and work. In particular, advances in artificial intelligence have allowed multiple UAVs to coordinate their operations and work together to accomplish various complex tasks, one of which is Federated Learning (FL). As a promising distributed learning paradigm, FL can be adopted well with the limited resources and dynamic network topology of UAV swarms. However, the current FL's training process relies on homogeneous data paradigms, which require distributed UAVs to hold the same structure data. This ideal hypothesis is can not apply to the heterogeneous UAV swarms. To tackle this problem, in this paper, we design a clustered federated learning (CFL) architecture, in which we cluster UAV swarms based on the similarities between the participants' optimization directions. Then, we formulate the model trading among model owners, cluster heads, and UAV workers as a three-stage Stackelberg game to optimize the allocation of the limited resources. We design a hierarchical reinforcement learning algorithm to search for the Stackelberg equilibrium under the clustered federated learning system. The performance evaluation demonstrates the uniqueness and stability of the proposed three-stage master-slave game under the clustered framework, as well as the convergence and effectiveness of the reinforcement learning algorithm.

Scaling Up Structural Clustering to Large Probabilistic Graphs Using Lyapunov Central Limit Theorem

Structural clustering is one of the most widely used graph clustering frameworks. In this paper, we focus on structural clustering of probabilistic graphs, which comes with significant computational challenges and has, so far, resisted efficient solutions that are able to scale to large graphs, e.g. the state-of-art can only handle graphs with a few million edges. We address the main bottleneck step of probabilistic structural clustering, computing the structural similarity of vertices based on their Jaccard similarity over the set of possible worlds of a given probabilistic graph. The state-of-art used Dynamic Programming, a quadratic run-time algorithm, that does not scale to pairs of vertices of high degree. In this paper we present a novel approach based on Lyapunov Central Limit Theorem. By using a carefully chosen set of random variables we are able to cast the computation of structural similarity to computing a one-tailed area under the Normal Distribution. Our approach has linear runtime as opposed to quadratic, and as such, it scales to much larger inputs. Extensive experiments show that our approach can handle massive graphs at web-scale which the state-of-art cannot.

Evolutionary Robust Clustering Over Time for Temporal Data.

In many clustering scenes, data samples' attribute values change over time. For such data, we are often interested in obtaining a partition for each time step and tracking the dynamic change of partitions. Normally, a smooth change is assumed for data to have a temporal smooth nature. Existing algorithms consider the temporal smoothness as an a priori preference and bias the search toward the preferred direction. This a priori manner leads to a risk of converging to an unexpected region because it is not always the case that a reasonable preference can be elicited given the little prior knowledge about the data. To address this issue, this article proposes a new clustering framework called evolutionary robust clustering over time. One significant innovation of the proposed framework is processing the temporal smoothness in an a posteriori manner, which avoids unexpected convergence that occurs in existing algorithms. Furthermore, the proposed framework automatically infers the a posteriori preference to temporal smoothness without data's affinity matrix and predefined parameters, which holds better applicability and efficiency. The effectiveness and efficiency of the proposed framework are confirmed by comparing with state-of-the-art algorithms on both synthetic and real datasets.

A combinatorial multi-armed bandit approach to correlation clustering

Given a graph whose edges are assigned positive-type and negative-type weights, the problem of correlation clustering aims at grouping the graph vertices so as to minimize (resp. maximize) the sum of negative-type (resp. positive-type) intra-cluster weights plus the sum of positive-type (resp. negative-type) inter-cluster weights. In correlation clustering, it is typically assumed that the weights are readily available. This is a rather strong hypothesis, which is unrealistic in several scenarios. To overcome this limitation, in this work we focus on the setting where edge weights of a correlation-clustering instance are unknown, and they have to be estimated in multiple rounds, while performing the clustering. The clustering solutions produced in the various rounds provide a feedback to properly adjust the weight estimates, and the goal is to maximize the cumulative quality of the clusterings. We tackle this problem by resorting to the reinforcement-learning paradigm, and, specifically, we design for the first time a Combinatorial Multi-Armed Bandit (CMAB) framework for correlation clustering. We provide a variety of contributions, namely (1) formulations of the minimization and maximization variants of correlation clustering in a CMAB setting; (2) adaptation of well-established CMAB algorithms to the correlation-clustering context; (3) regret analyses to theoretically bound the accuracy of these algorithms; (4) design of further (heuristic) algorithms to have the probability constraint satisfied at every round (key condition to soundly adopt efficient yet effective algorithms for correlation clustering as CMAB oracles); (5) extensive experimental comparison among a variety of both CMAB and non-CMAB approaches for correlation clustering.

Effective sample pairs based contrastive learning for clustering

As an indispensable branch of unsupervised learning, deep clustering is rapidly emerging along with the growth of deep neural networks. Recently, contrastive learning paradigm has been combined with deep clustering to achieve more competitive performance. However, previous works mostly employ random augmentations to construct sample pairs for contrastive clustering. Different augmentations of a sample are treated as positive sample pairs, which may result in false positives and ignore the semantic variations of different samples. To address these limitations, we present a novel end-to-end contrastive clustering framework termed Contrastive Clustering with Effective Sample pairs construction (CCES), which obtains more semantic information by jointly leveraging an effective data augmentation method ContrastiveCrop and constructing positive sample pairs based on nearest-neighbor mining. Specifically, we augment original samples by adopting ContrastiveCrop, which explicitly reduces false positives and enlarges the variance of samples. Further, with the extracted feature representations, we provide a strategy to construct positive sample pairs via a sample and its nearest neighbor for instance-wise and cluster-wise contrastive learning. Experimental results on four challenging datasets demonstrate the effectiveness of CCES for clustering, which surpasses the state-of-the-art deep clustering methods.

Leveraging similarity analysis to understand variability in movement behavior

AbstractThe increasingly large volume of trajectories of moving entities obtained through GPS and cellphone tracking, telemetry, and other location‐aware technologies motivates researchers to understand the implicit patterns hidden in movement trajectories and understand how movement is influenced by the environmental context. Trajectory similarity serves as an important tool in computational movement analysis and as the foundation of revealing those patterns. However, there are various trajectory similarity measures, each of which has its own strengths and weaknesses. In this article, we present a hierarchical clustering framework that integrates five commonly used similarity measures, including Fréchet distance, dynamic time warping, Hausdorff distance, longest common subsequence, and normalized weighted edit distance, a special kind of edit distance for movement analysis. The framework aims at clustering similar patterns and identifying variability in movement. The optimal number of clusters are first obtained. Then, the clusters are characterized by environmental variables to explore the associations between variability in movement and the environmental conditions. We evaluate the proposed framework using 15 years of tracking data of turkey vultures, tracked at 1‐ to 3‐h sampling intervals, during their fall and spring migration seasons. The results suggest that, at 5% significance level, turkey vultures select their movement paths intentionally and those selections appear to be related to certain environmental context variables, including thermal uplift, vegetation state (observed indirectly through Normalized Difference Vegetation Index), temperature, precipitation, tailwind, and crosswind. And interestingly, there exist preferential differences among individuals. Although the preference of the same turkey vulture is not strictly consistent over different years, each individual tends to preserve a more similar preference over different years, compared with the preferences of other turkey vultures.

Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces

Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters. This is especially effective in scenarios where separate groups of clients have significant differences in the distributions of their local data. Existing clustered FL algorithms are essentially trying to group together clients with similar distributions so that clients in the same cluster can leverage each other's data to better perform federated learning. However, prior clustered FL algorithms attempt to learn these distribution similarities indirectly during training, which can be quite time consuming as many rounds of federated learning may be required until the formation of clusters is stabilized. In this paper, we propose a new approach to federated learning that directly aims to efficiently identify distribution similarities among clients by analyzing the principal angles between the client data subspaces. Each client applies a truncated singular value decomposition (SVD) step on its local data in a single-shot manner to derive a small set of principal vectors, which provides a signature that succinctly captures the main characteristics of the underlying distribution. This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters. This is achieved by comparing the similarities of the principal angles between the client data subspaces spanned by those principal vectors. The approach provides a simple, yet effective clustered FL framework that addresses a broad range of data heterogeneity issues beyond simpler forms of Non-IIDness like label skews. Our clustered FL approach also enables convergence guarantees for non-convex objectives.

GLCC: A General Framework for Graph-Level Clustering

This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein clustering and genome analysis in bioinformatics. Recent years have witnessed the success of deep clustering coupled with graph neural networks (GNNs). However, existing methods focus on clustering among nodes given a single graph, while exploring clustering on multiple graphs is still under-explored. In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs. Specifically, GLCC first constructs an adaptive affinity graph to explore instance- and cluster-level contrastive learning (CL). Instance-level CL leverages graph Laplacian based contrastive loss to learn clustering-friendly representations while cluster-level CL captures discriminative cluster representations incorporating neighbor information of each sample. Moreover, we utilize neighbor-aware pseudo-labels to reward the optimization of representation learning. The two steps can be alternatively trained to collaborate and benefit each other. Experiments on a range of well-known datasets demonstrate the superiority of our proposed GLCC over competitive baselines.

Scalable and Effective Conductance-Based Graph Clustering

Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph analysis applications. Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities. To overcome these limitations, we devise a powerful peeling-based graph clustering framework PCon. We show that many existing solutions can be reduced to our framework. Namely, they first define a score function for each vertex, then iteratively remove the vertex with the smallest score. Finally, they output the result with the smallest conductance during the peeling process. Based on our framework, we propose two novel algorithms PCon_core and PCon_de with linear time and space complexity, which can efficiently and effectively identify clusters from massive graphs with more than a few billion edges. Surprisingly, we prove that PCon_de can identify clusters with near-constant approximation ratio, resulting in an important theoretical improvement over the well-known quadratic Cheeger bound. Empirical results on real-life and synthetic datasets show that our algorithms can achieve 5~42 times speedup with a high clustering accuracy, while using 1.4~7.8 times less memory than the baseline algorithms.

Exo‐endo Isomerism of Carboranes: Unusual Geometries of C2B5X7 (X=Cl, Br) and C2B7Cl9 with exo‐Skeletal BCl2 Groups on Carbon Revealed by Joint Spectroscopic/Computational Studies

AbstractReexamination of the co‐pyrolysis reactions of B2Cl4 with C2Cl4 at 350 °C and of B2Br4 with CBr4 at 300 °C in vacuo confirmed the carboranes C2B5Cl7 (1), C2B7Cl9 (2), and C2B5Br7 (3) as low‐yield products. While 1 only could be concentrated by repeated vacuum fractionation, 2 and 3 could now be isolated from the conglomerate mixtures for a full spectroscopic characterization and the compounds were verified in their geometries by detailed DFT computations. Surprisingly, the perhalogenated carboranes do not adopt the expected “all‐endo”‐geometries with cluster sizes derived by the sum of the n boron and two carbon atoms (n+2) as known from the syntheses of the parent closo‐carboranes C2BnHn+2. Instead, DFT/GIAO(ZORA)/NMR (GIAO for X=Cl, ZORA for X=Br) computations revealed that the perhalogenated carboranes favor structures with BX2 groups as exo‐skeletal ligands attached to both cage‐carbon atoms yielding the five‐vertex closo‐1,5‐(CBX2)2B3X3 (1: X=Cl; 3: X=Br) and the seven‐vertex closo‐2,4‐(CBCl2)2B5Cl5 for 2. In contrast to these perhalogenated carboranes, analogous computations on the hydrogen substituted carboranes C2BnHn+2, silaboranes Si2BnHn+2 and Si2BnXn+2 (n=5, 7) show in all cases a thermodynamic favorization of structures where all boron atoms of the formula are endo‐skeletally incorporated into the cluster frameworks.

Entropy-based grid approach for handling outliers: a case study to environmental monitoring data.

Grid-based approaches render an efficient framework for data clustering in the presence of incomplete, inexplicit, and uncertain data. This paper proposes an entropy-based grid approach (EGO) for outlier detection in clustered data. The given hard clusters obtained from a hard clustering algorithm, EGO uses entropy on the dataset as a whole or on an individual cluster to detect outliers. EGO works in two steps: explicit outlier detection and implicit outlier detection. Explicit outlier detection is concerned with those data points that are isolated in the grid cells. They are either far from the dense region or maybe a nearby isolated data point and therefore declared as an explicit outlier. Implicit outlier detection is associated with the detection of outliers that are perplexedly deviated from the normal pattern. The determination of such outliers is achieved using entropy change of the dataset or a specific cluster for each deviation. The elbow based on the trade-off between entropy and object geometries optimizes the outlier detection process. Experimental results on CHAMELEON datasets and other similar datasets suggested that the proposed approach(es) detect the outliers more precisely and extend the capability of outliers detection to an additional 4.5% to 8.6%. Moreover, the resultant clusters became more precise and compact when the entropy-based gridding approach is applied on top of hard clustering algorithms. The performance of the proposed algorithms is compared with well-known outlier detection algorithms, including DBSCAN, HDBSCAN, RE3WC, LOF, LoOP, ABOD, CBLOF and HBOS. Finally, a case study for detecting outliers in environmental data has been carried out using the proposed approach and results are generated on our synthetically prepared datasets. The performance shows that the proposed approach may be an industrial-oriented solution to outlier detection in environmental monitoring data.

Time series clustering-enabled geological condition perception in tunnel boring machine excavation

For automatic geology perception, this paper develops a DTW-Kmedoids-enabled time series clustering framework to gain a clear insight into the geological condition ahead of the tunnel boring machine (TBM) head. The core idea of DTW-Kmedoids is to incorporate dynamic time warping (DTW) as a distance metric into a popular clustering algorithm Kmedoids, aiming to aggregate time series data reflecting similar conditions of surrounding geology into the same cluster. It has been verified in a sequence dataset recording 377 operational rings from a TBM tunneling project in Singapore. Results indicate that the proposed DTW-Kmedoids algorithm outperforms Kmedoids and softDTW-Kmedoids in partitioning the whole dataset into four meaningful clusters on behalf of four geological conditions. Moreover, it shows merits in great robustness to handle varying-length temporal sequences and even fragmentary data instead of the full observations. Furthermore, an online clustering mechanism is well designed to adaptively update the cluster center, which can accomplish the dynamic recognition of the forward geological circumstances ring by ring. In short, the practical value behind the hybrid time series clustering is to automatically and quickly perceive lithology categories along the designed tunnel trail with no requirement of data labeling, possibly allowing for further inference of geological risk and guidance in safe excavation.

Mobility Assisted Adaptive Clustering Hierarchy for IoT Based Sensor Networks in 5G and Beyond

One of the massive machine type communication (mMTC) applications for monitoring and sensing in 5G cellular network is the Internet of Things (IoT) based wireless sensor network (WSN). Non uniform battery usage by the nodes in these networks often results in creating energy holes or voids in the network making the network disconnected. An effective solution is to deploy multiple mobile nodes throughout the network, however finding optimal path for these mobile nodes is reported to be an NP hard problem. This paper proposes MAACH (Mobility Assisted Adaptive Clustering Hierarchy), an efficient mobility assisted clustering and routing framework for IoT based sensor network in 5G and beyond. Also, an elaborate method to calculate the exact path optimally for multiple mobile nodes is presented to alleviate non uniform energy dissipation of the sensing nodes. Simulation results show that our algorithm effectively finds the optimal trajectory of multiple mobile nodes in a distribute manner and also improves network stability period by 60-70% and the network lifetime by 70-90% across multiple network deployments.

Efficient and scalable DBSCAN framework for clustering continuous trajectories in road networks

Clustering the trajectories of vehicles moving on road networks is a key data mining technique for understanding human mobility patterns, as well as their interactions with urban environments. The development of efficient and scalable trajectory clustering algorithms, however, still faces challenges because of the computational costs when measuring similarities among a large number of network-constrained trajectories. To address this problem, a novel trajectory clustering framework based on the well-developed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach is proposed. This proposed framework accurately quantifies similarities using a trajectory representation of continuous polylines in the space and time dimensions, and does not require trajectory discretization. Further, the proposed framework utilizes the space-time buffering concept to formulate ε -neighborhood queries that directly retrieve the ε -neighbors of trajectories and thus avoids computing a trajectory similarity matrix. State-of-the-art trajectory databases and index structures are incorporated to further improve trajectory clustering performance. A comprehensive case study was carried out using an open dataset of 20,161 trajectories. Results show that the proposed framework efficiently executed trajectory clustering on the large test dataset within 3 min. This was approximately 2,700 times faster than existing DBSCAN algorithms.

Graph-Based Soft-Balanced Fuzzy Clustering

Spectral clustering have attracted more and more attention due to their well-defined mathematical frameworks and superior performance. However, there still exist two limitations to be solved: 1) most spectral clustering methods consist of two independent stages, which may cause unpredictable deviation of obtained clustering results from the genuine ones and lead to severe information loss and performance degradation; 2) spectral clustering methods employ the hard clustering mode, which lacks interpretability for data points in the boundary area belonging to multiple clusters. To simultaneously address these challenging issues for spectral clustering, we propose a Graph based soft-Balanced Fuzzy Clustering (GBFC) model. Specifically, we explicitly preserve the nonnegative property of the clustering indicator matrix to enhance the interpretability of clustering results. Moreover, row normalization is imposed on the cluster indicator matrix to show the membership of each data point to different clusters. Additionally, a novel balanced constraint is designed to regularize the clustering results and constrain the size of clusters. We can directly obtain the clustering assignments without any post-processing, and the limitations of the previous two-stage clustering framework can be effectively addressed. Extensive experiments performed on both synthetic datasets and real world datasets demonstrate the superiority and effectiveness of the proposed algorithm compared with several state-of-the-art methods.

Europium enrichment and hierarchical formation of the Galactic halo

Context.The origin of the large star-to-star variation of the [Eu/Fe] ratios observed in the extremely metal-poor (at [Fe/H] ≤ −3) stars of the Galactic halo is still a matter of debate.Aims.In this paper, we explore this problem by putting our stochastic chemical evolution model in the hierarchical clustering framework, with the aim of explaining the observed spread in the halo.Methods.We compute the chemical enrichment of Eu occurring in the building blocks that have possibly formed the Galactic halo. In this framework, the enrichment from neutron star mergers can be influenced by the dynamics of the binary systems in the gravitational potential of the original host galaxy. In the least massive systems, the neutron stars can merge outside the host galaxy and so only a small fraction of newly produced Eu can be retained by the parent galaxy itself.Results.In the framework of this new scenario, the accreted merging neutron stars are able to explain the presence of stars with sub-solar [Eu/Fe] ratios at [Fe/H] ≤ −3, but only if we assume a delay time distribution for merging of the neutron stars ∝t−1.5. We confirm the correlation between the dispersion of [Eu/Fe] at a given metallicity and the fraction of massive stars which give origin to neutron star mergers. The mixed scenario, where both neutron star mergers and magneto-rotational supernovae do produce Eu, can explain the observed spread in the Eu abundance also for a delay time distribution for mergers going either as ∝t−1or ∝t−1.5.