Advanced Clustering Algorithm Research Articles

Scenario-based stochastic optimization on the variability of solar and wind for component sizing of integrated energy systems

The inherent intermittency and variability of renewable energy sources present significant challenges to the optimal design and implementation of integrated energy systems (IES). This paper introduces a novel stochastic optimization model that integrates advanced scenario generation and clustering algorithm for renewable energy sources within a multi-objective, bi-level optimization framework. Specifically, the clearness index is employed to represent the stochastic distribution of solar radiation intensity by beta distribution, while wind speed uncertainty is modeled seasonally using the Weibull distribution. Monte Carlo sampling with synchronous back substitution is applied for scenario generation and reduction of solar radiation and wind speed. To address the multi-objective evaluation, the analytic hierarchy process is utilized, and the joint optimization is achieved by combining a region contraction algorithm with stochastic programming. The proposed methodology is validated on an IES featuring various heating devices, incorporating uncertainties in both wind and solar energy. The results indicate that the absorption heat pump-based scheme achieves superior energy-saving performance, achieving an energy rate of 0.4724. Additionally, the compression heat pump-based scheme exhibits excellent economic efficiency and environmental sustainability, with a cost of energy of 0.3639 and a renewable fraction of 0.5536.

Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering.

By merging advanced dimensionality reduction (DR) and clustering algorithm (CA) techniques, our study advances the sampling procedure for predicting NMR chemical shifts (CS) in intrinsically disordered proteins (IDPs), making a significant leap forward in the field of protein analysis/modeling. We enhance NMR CS sampling by generating clustered ensembles that accurately reflect the different properties and phenomena encapsulated by the IDP trajectories. This investigation critically assessed different rapid CS predictors, both neural network (e.g., Sparta+ and ShiftX2) and database-driven (ProCS-15), and highlighted the need for more advanced quantum calculations and the subsequent need for more tractable-sized conformational ensembles. Although neural network CS predictors outperformed ProCS-15 for all atoms, all tools showed poor agreement with HN CSs, and the neural network CS predictors were unable to capture the influence of phosphorylated residues, highly relevant for IDPs. This study also addressed the limitations of using direct clustering with collective variables, such as the widespread implementation of the GROMOS algorithm. Clustered ensembles (CEs) produced by this algorithm showed poor performance with chemical shifts compared to sequential ensembles (SEs) of similar size. Instead, we implement a multiscale DR and CA approach and explore the challenges and limitations of applying these algorithms to obtain more robust and tractable CEs. The novel feature of this investigation is the use of solvent-accessible surface area (SASA) as one of the fingerprints for DR alongside previously investigated α carbon distance/angles or ϕ/ψ dihedral angles. The ensembles produced with SASA tSNE DR produced CEs better aligned with the experimental CS of between 0.17 and 0.36 r2 (0.18-0.26 ppm) depending on the system and replicate. Furthermore, this technique produced CEs with better agreement than traditional SEs in 85.7% of all ensemble sizes. This study investigates the quality of ensembles produced based on different input features, comparing latent spaces produced by linear vs nonlinear DR techniques and a novel integrated silhouette score scanning protocol for tSNE DR.

Design of health information management model for elderly care using an advanced higher-order hybrid clustering algorithm from the perspective of sports and medicine integration.

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population.

Enhancing Residential Electricity Safety and Management: A Novel Non-Intrusive Load Monitoring-Based Methodology for Accurate Appliance Operational State Identification

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology.

An efficient feature selection algorithm for health care data analysis

Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.

WEDMS: An advanced mean shift clustering algorithm for LDoS attacks detection

Network and communication security are the focus of attention. Low-rate denial of service (LDoS) attacks exploit deficiencies of TCP protocol to restrain TCP throughput and network quality of links, by sending pulse sequences periodically. It is difficult for the defense against LDoS attacks by the available DoS attacks detection methods, due to the low average rate and prodigious concealment of LDoS attacks, which threats on the network security seriously. In this paper, a new approach for LDoS attacks detection based on the advanced Mean Shift clustering algorithm with weighted Euclidean distance (WEDMS) is proposed. Based on the distinction that the discreteness of network traffic suffering LDoS attacks is more obvious than that of legitimate traffic, network traffic can be clustered by the WEDMS algorithm. After cluster analysis, the existence of LDoS attacks can be validated according to the decision feature of the clustering results. Experiments on detection performance are carried out in NS-2, test-bed, and public datasets such as LBNL, WIDE2006, and WIDE2018. The experimental results illustrate that the presence of LDoS attacks can be identified by the proposed method with higher TPR and lower FPR.

Smart Meter Data Based Load Forecasting and Demand Side Management in Distribution Networks With Embedded PV Systems

With a significant deployment of smart meters across end-user platforms, the dynamic visibility of energy flow among the end-users has been increased significantly. The granular information of smart meters can be used to improve the load forecast accuracy and to influence energy consumption patterns with demand side management (DSM) schemes. This paper addresses the challenges of smart meter data size, complexity, variability and volatility for efficient use in load forecast and DSM. A novel clustering-based approach for analysis of smart meter data, aimed at more accurate and detailed load profiling, reduced profile complexity, improved load forecast accuracy and providing optimal DSM solutions is proposed. The proposed approach utilizes an advanced clustering algorithm to reduce the data size. The approach addresses data complexity, variability and volatility by linearizing the load profiles and minimizing the errors. The validity of the approach is demonstrated on an Irish smart meter dataset and on a simulated solar photovoltaic (PV) data and showed an improved load forecast accuracy, improved DSM solutions, and reduced computational burden. The improvements in the DSM solution are evidenced by a higher cost saving with a higher peak load reduction at the lower level of demand flexibility.

A Review of Various Multiple Numerical and Categorical Sensitive Attribute for Preserving Privacy

Nowadays Privacy Preserving in Data Publishing (PPDP) has rapidly growth into the research contents in data protection field and also it has rapidly grown in publication of personal data. Recent years in data publishing is exceptionally analytical, because, how to economically preserve numerical and categorical sensitive attribute. However, different tender have been designed for privacy preserving to protect numerical sensitive attribute in data publishing, Like k-anonymity, l-diversity, t-closeness, (epsilon, m)-anonymity another methods are implemented for protecting the privacy of data provider. In this paper, we propose a review of various multiple numerical and categorical sensitive attribute for preserving privacy and survey current existing techniques. Yet, we discuss the future instructions of privacy preserving in data publishing, that is we suggest modern technique for “A new enhanced slicing method for both numerical and categorical sensitive attribute via advanced clustering algorithm” that improving the privacy with less information loss, membership, attribute , identity disclosure and error ratio and also calculate distance between two attributes for categorical attribute. This technique suitable for both categorical and numerical sensitive attributes.

PRIVACY PRESERVING DATA MINING USING MULTIPLE OBJECTIVE OPTIMIZATION

Privacy preservation is that the most targeted issue in information publication, because the sensitive data shouldn't be leaked. For this sake, several privacy preservation data mining algorithms are proposed. In this work, feature selection using evolutionary algorithm and data masking coupled with slicing is treated as a multiple objective optimisation to preserve privacy. To start with, Genetic Algorithm (GA) is carried out over the datasets to perceive the sensitive attributes and prioritise the attributes for treatment as per their determined sensitive level. In the next phase, to distort the data, noise is added to the higher level sensitive value using Hybrid Data Transformation (HDT)method. In the following phase slicing algorithm groups the correlated attributes organized and by this means reduces the dimensionality by retaining the Advanced Clustering Algorithm (ACA). With the aim of getting the optimal dimensions of buckets, tuple segregating is accomplished by Metaheuristic Firefly Algorithm (MFA). The investigational consequences imply that the anticipated technique can reserve confidentiality and therefore the information utility is additionally high. Slicing algorithm allows the protection of association and usefulness in which effects in decreasing the information dimensionality and information loss. Performance analysis is created over OCC 7 and OCC 15 and our optimization method proves its effectiveness over two totally different datasets by showing 92.98% and 96.92% respectively.

Picture fuzzy clustering for complex data

Fuzzy clustering is a useful segmentation tool which has been widely used in many applications in real life problems such as in pattern recognition, recommender systems, forecasting, etc. Fuzzy clustering algorithm on picture fuzzy set (FC-PFS) is an advanced fuzzy clustering algorithm constructed on the basis of picture fuzzy set with the appearance of three membership degrees namely the positive, the neutral and the refusal degrees combined within an entropy component in the objective function to handle the problem of incomplete modeling in fuzzy clustering. A disadvantage of FC-PFS is its capability to handle complex data which include mix data type (categorical and numerical data) and distinct structured data. In this paper, we propose a novel picture fuzzy clustering algorithm for complex data called PFCA-CD that deals with both mix data type and distinct data structures. The idea of this method is the modification of FC-PFS, using a new measurement for categorical attributes, multiple centers of one cluster and an evolutionary strategy – particle swarm optimization. Experiments indicate that the proposed algorithm results in better clustering quality than others through clustering validity indices.

Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes

An enormous quantity of personal health information is available in recent decades and tampering of any part of this information imposes a great risk to the health care field. Existing anonymization methods are only apt for single sensitive and low dimensional data to keep up with privacy specifically like generalization and bucketization. In this paper, an anonymization technique is proposed that is a combination of the benefits of anatomization, and enhanced slicing approach adhering to the principle of k-anonymity and l-diversity for the purpose of dealing with high dimensional data along with multiple sensitive data. The anatomization approach dissociates the correlation observed between the quasi identifier attributes and sensitive attributes (SA) and yields two separate tables with non-overlapping attributes. In the enhanced slicing algorithm, vertical partitioning does the grouping of the correlated SA in ST together and thereby minimizes the dimensionality by employing the advanced clustering algorithm. In order to get the optimal size of buckets, tuple partitioning is conducted by MFA. The experimental outcomes indicate that the proposed method can preserve privacy of data with numerous SA. The anatomization approach minimizes the loss of information and slicing algorithm helps in the preservation of correlation and utility which in turn results in reducing the data dimensionality and information loss. The advanced clustering algorithms prove its efficiency by minimizing the time and complexity. Furthermore, this work sticks to the principle of k-anonymity, l-diversity and thus avoids privacy threats like membership, identity and attributes disclosure.

Statistical investigation of surface bound ions and further development of BION server to include pH and salt dependence.

Ions are engaged in multiple biological processes in cells. By binding to the macromolecules or being mobile in the solvent, they maintain the integrity of the structure of macromolecules; participate in their enzymatic activity; or screen electrostatic interactions. While experimental methods are not always able to assign the exact location of ions, computational methods are in demand. Although the majority of computational methods are successful in predicting the position of ions buried inside macromolecules, they are less effective in deciphering positions of surface bound ions. Here, we propose the new BION algorithm (http://compbio.clemson.edu/bion_server_ph/) that predicts the location of the surface bound ions. It is more efficient and accurate compared to the previous version since it uses more advanced clustering algorithm in combination with pairing rules. In addition, the BION webserver allows specifying the pH and the salt concentration in predicting ions positions.

An Advanced Clustering Algorithm (ACA) for Clustering Large Data Set to Achieve High Dimensionality

Cluster analysis method is one of the main analytical methods in data mining; this method of clustering algorithm will influence the clustering results directly. This paper proposes an Advanced Clustering Algorithm in order to solve the question of high dimensionality and large data set. The Advanced Clustering Algorithm method avoids computing the distance of each data object to the cluster centers again and again and save the running time. ACA requires a simple data structure to store information in every iteration, which is to be used in the next iteration. Experimental results show that the Advanced Clustering Algorithm method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the traditional algorithms (K-Means, SOM and HAC). This paper includes Advanced Clustering Algorithm (ACA) and its experimental results through experimenting with academic data sets.

Dual-Stage Pseudo Power Gating with Advanced Clustering Algorithm for Gate Level Power Optimization

Dual-Stage Pseudo Power Gating with Advanced Clustering Algorithm for Gate Level Power Optimization

HD-Eye: visual mining of high-dimensional data

Clustering in high-dimensional databases poses an important problem. However, we can apply a number of different clustering algorithms to high-dimensional data. The authors consider how an advanced clustering algorithm combined with new visualization methods interactively clusters data more effectively. Experiments show these techniques improve the data mining process.

An operation sequence based similarity coefficient for part families formations

Abstract To date, similarity of parts in group technology research has been defined and operationalized primarily on the basis of the machine requirements of parts. In this paper, a similarity coefficient based on the similarity of operation sequences is presented, and its use for parts grouping is discussed. It is also illustrated that such a coefficient, augmented with an advanced clustering algorithm, can improve production effectiveness by identifying part families that allow machines to interleave between identical operations of different parts.