Optimal Set Of Clusters Research Articles

Federated learning with SARIMA-based clustering for carbon emission prediction

Driven by the global Sustainable Development Goals, predicting the long-term trend of carbon emissions in the industrial sector has important reference value for managing national macroeconomic and reducing carbon emissions. To address the potential privacy leakage problem when using multisource industrial big data for carbon emission prediction, a federated learning method was introduced for carbon emission prediction research. However, owing to the variability in individual data providers, using all clients indiscriminately to train the federated model may hinder the computational efficiency of federated learning. To improve the accuracy and computational efficiency of the prediction model, a federated learning method based on SARIMA clustering was proposed. First, a trend was fitted to all clients based on the SARIMA model and the clients were grouped into different clusters based on the fitting results. Second, a federated bidirectional long short-term memory calculation was implemented for different client combinations using a federated averaging algorithm. Extensive experimental results on real datasets show that combining SARIMA and the federated averaging algorithm protects the data privacy of clients and improves the convergence speed and accuracy of the federated learning carbon emission prediction model. Within the optimal set of clusters (Cluster 0), the mean absolute error and mean square error improved by 63.32% and 79.27%, respectively, and the convergence speed of the model improved by 73.17%. This study further enriches the methodological system of carbon emission prediction and provides methodological support for relevant departments to use multisource big data in the industry for carbon emission prediction.

Energy-efficient IoT based improved health monitoring system for sports persons

Nowadays, wearable technology and the Internet of Things (IoT) are transforming the healthcare sector by refining the way how devices, applications, and people connect and interact with each other. IoT applications in sports are tremendously useful to monitor health and reduce the risk factor. The battery life of wearable and accurate monitoring has been considered a significant challenge in sports medicine. Hence, in this paper, Energy Efficient IoT based Improved Health Monitoring system (EEIoT-IHMS) has been proposed for accurate and continuous sports person’s health monitoring system. This paper determines the optimal set of clusters based on sensor features, in which power usage has been minimized by duty cycling with optimized prediction accuracy. The experimental results demonstrate that the proposed (EEIoT-IHMS) enhances accuracy ratio, improves battery life, and reduces energy consumption compared to other popular methods.

Variants of bat algorithm for solving partitional clustering problems

Clustering is an exploratory data analysis technique that organize the data objects into clusters with optimal distance efficacy. In this work, a bat algorithm is considered to obtain optimal set of clusters. The bat algorithm is based on the echolocation feature of micro bats. Moreover, some improvements are proposed to overcome the shortcoming associated with bat algorithm like local optima, slow convergence, initial seed points and trade-off between local and global search mechanisms etc. An enhanced cooperative co-evolution method is proposed for addressing the initial seed points selection issue. The local optima issue is handled through neighbourhood search-based mechanism. The trade-off issue among local and global searches of bat algorithm is addressed through a modified elitist strategy. On the basis of aforementioned improvements, three variants (BA-C, BA-CN and BA-CNE) of bat algorithm is developed and efficacy of these variants is tested over twelve benchmark clustering datasets suing intra-cluster distance, accuracy and rand index parameters. Simulation results showed that BA-CNE variant achieves more effective clustering results as compared to BA-C, BA-CN and BA. The simulation results of BA-CNE are also compared with several existing clustering algorithms and two statistical tests are also applied to investigate the statistical difference among BA-CNE and other clustering algorithms. The simulation and statistical results confirmed that BA-CNE is an effective and robust algorithm for handling partitional clustering problems.

Graph Theory Based Voltage Sag Mitigation Cluster Formation Utilizing Dynamic Voltage Restorers in Radial Distribution Networks

Voltage sag mitigation utilizing dynamic voltage restorers (DVRs) can be classified as a common-pool resource (CPR) good. However, the ability of DVRs in improving voltage sag performance of only downstream customers provides the ability to exclude the non-contributors selectively. Therefore, unlike traditional CPR goods, the DVR allocation problem can give rise to partial excludability. Here, the non-collocated customers have been divided into feasible clusters through suitable positioning of DVRs using the proposed graph-partitioning principle. In the absence of trustworthiness, especially with the participation of electricity supply companies, the participants may ask an external agent and share their willingness-to-pay information to design the optimal set of clusters. Alternatively, the customers, including electricity supply companies, can also share internal information as an open system. Strategies for sharing internal information to avoid free-riding are also discussed. The utility distribution would ensure the viability of contribution group formation. Here, the results from three different utility-distributing solution concepts, such as an alternative definition of the core, the nucleous, and Shapley value, are compared. The simulation results are numerically verified for a small scale system and validated utilizing a large scale system. The proposed methodology can be used for any real-world system that follows similar properties.

A neighborhood search based cat swarm optimization algorithm for clustering problems

Clustering is an unsupervised technique that groups the similar data objects into a single subset using a distance function. It is also used to find the optimal set of clusters in a given dataset and each cluster consists of homogenous data objects. In present work, an algorithm based on cat swarm optimization (CSO) is adopted for finding the optimal set of cluster centers for allocating the data objects. Further, some improvements are also incorporated in CSO algorithm for improving clustering performance. These modifications are described as an improved solution search equation to improve convergence rate and an accelerated velocity equation for balancing exploration and exploitation processes of CSO algorithm. Moreover, a neighborhood-based search strategy is introduced to handle local optima problem. The performance of proposed algorithm is tested on eight real-life datasets and compared with well-known clustering algorithms. The simulation results showed that proposed algorithm provides quality results in comparison to existing clustering algorithms.

A framework for crime data analysis using relationship among named entities

Many crime reports are available online in various blogs and Newswire. Though manual annotation of these massive reports is quite tedious for crime data analysis, it gives an overall crime scenario of all over the world. This motivates us to propose a framework for crime data analysis based on the online reports. Initially, the method extracts the crime reports and identifies named entities. The intermediate sequence of context words between every consecutive pair of named entities is termed as a crime vector that provides relationships between the entities. The feature vectors for each entity pair are generated from these crime vectors using the Word2Vec model. The paper considers three different types of named entity pairs to facilitate the major crime data analysis task, and for each type, similarity between every pair of entities is measured using respective feature vectors. For each type of named entity pair, a separate weighted graph is generated with entity pairs as vertices and similarity score between them as the weight of the corresponding edge. Then, Infomap, a graph-based clustering algorithm, is applied to obtain optimal set of clusters of entity pairs and a representative entity pair of each cluster. Each cluster is labelled by the relationship, represented by the crime vector, of its representative entity pair. In reality, all the entity pairs in a cluster may not reflect contextual similarity with their representative entity pair. So the clusters are further partitioned into subclusters based on WordNet-based path similarity measure which makes the entity pairs in each subcluster more contextually similar compared to their original cluster. These subclusters provide us various statistical crime information over the time period. The method is experimented only using the crime reports related to crime against women in India. The experimental results demonstrate the effectiveness and superiority of the method compared to others for analysing the crime data.

Adaptive Simulated Evolution based Approach for Cluster Optimization in Wireless Sensor Networks

Energy consumption minimization is crucial for the constrained sensors in wireless sensor networks (WSNs). Partitioning WSNs into optimal set of clusters is a promising technique utilized to minimize energy consumption and to increase the lifetime of the network. However, optimizing the network into optimal set of clusters is a non-polynomial (NP) hard problem, and the time needed to solve such problem increases exponentially as the number of sensors increases. In this paper, simulated evolution (SimE) algorithm is engineered to tackle the problem of cluster optimization in WSNs. A goodness measure is developed to measure the accuracy of assigning nodes to clusters and to evaluate the clustering quality of the overall network. SimE was developed such that the number of clusters and cluster heads are adaptive to number of alive nodes in the network. In fact, extensive simulation results demonstrate that SimE provides near optimal clustering and improves the lifetime of the network by about 21% compared to the traditional LEACH-C protocol.

Cluster Optimization Based on Metaheuristic Algorithms in Wireless Sensor Networks

Partition of networks into optimal set of clusters is the prominent technique to prolong the network lifetime of energy constrained wireless sensor networks. Enumeration search method cannot find optimal clusters within polynomial bounded time for large scale networks since the computational complexity of problem grows exponentially with the dimension of networks. Optimal cluster configuration in sensor networks is known to be Non-deterministic Polynomial (NP)-hard optimization problem and for that reason we have applied polynomial time metaheuristic algorithms to find optimal or near-optimal solutions. In this paper, we present clustering algorithms based on Simulated Annealing (SA) and Particle Swarm Optimization (PSO) to find optimal set of cluster heads in the network. The optimization problem consists of finding optimal configuration of clusters such that the communication distance per cluster is not only minimized but the cluster balance and energy efficiency is also maintained in the network. The SA and PSO toolboxes are developed in C++ and integrated with OMNeT++ simulation environment to implement the proposed clustering algorithms. The performance of algorithms with respect to network lifetime, load balance and energy efficiency of network is examined in the simulation.

Eratosthenes sieve based key-frame extraction technique for event summarization in videos

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. It is a herculean task to manage access to video content in real time where humongous amount of audiovisual recorded data is generated every second. In this paper we propose an Eratosthenes Sieve based key-frame extraction approach for video summarization (VS) which can work better for real-time applications. Here, Eratosthenes Sieve is used to generate sets of all Prime number frames and nonprime number frames up to total N frames of a video. k-means clustering procedure is employed on these sets to extract the key–frames quickly. Here, the challenge is to find the optimal set of clusters, achieved by employing Davies-Bouldin Index (DBI). DBI a cluster validation technique which allows users with free parameter based VS approach to choose the desired number of key-frames without incurring additional computational costs. Moreover, our proposed approach includes likes of both local and global perspective videos. The method strongly enhances clustering procedure performance trough engagement of Eratosthenes Sieve. Qualitative and quantitative evaluation and complexity computation are done in order to compare the performances of the proposed model and state-of-the-art models. Experimental results on two benchmark datasets with various types of videos exhibit that the proposed methods outperform the state-of-the-art models on F-measure.

Modified CURE algorithm with enhancement to identify number of clusters

In this paper, we present an effective way of identifying number of clusters k based on density of data in given dataset and optimality of clusters formed. We have used internal evaluation of clustering to choose optimal set of clusters after narrowing the selection space to a small ideal range. This range is identified by partitioning dataset into a number of partitions using kd-tree so that partitions created contains densely packed data objects. We have used the concept of multi-representative points of a cluster in partitioning and evaluation of clustering and implemented it by modifying CURE algorithm. In this paper, linear transformation method PCA is applied to reduce high dimensional data into lower dimensions.

Brain network characterization of high-risk preterm-born school-age children.

Higher risk for long-term cognitive and behavioral impairments is one of the hallmarks of extreme prematurity (EP) and pregnancy-associated fetal adverse conditions such as intrauterine growth restriction (IUGR). While neurodevelopmental delay and abnormal brain function occur in the absence of overt brain lesions, these conditions have been recently associated with changes in microstructural brain development. Recent imaging studies indicate changes in brain connectivity, in particular involving the white matter fibers belonging to the cortico-basal ganglia-thalamic loop. Furthermore, EP and IUGR have been related to altered brain network architecture in childhood, with reduced network global capacity, global efficiency and average nodal strength. In this study, we used a connectome analysis to characterize the structural brain networks of these children, with a special focus on their topological organization. On one hand, we confirm the reduced averaged network node degree and strength due to EP and IUGR. On the other, the decomposition of the brain networks in an optimal set of clusters remained substantially different among groups, talking in favor of a different network community structure. However, and despite the different community structure, the brain networks of these high-risk school-age children maintained the typical small-world, rich-club and modularity characteristics in all cases. Thus, our results suggest that brain reorganizes after EP and IUGR, prioritizing a tight modular structure, to maintain the small-world, rich-club and modularity characteristics. By themselves, both extreme prematurity and IUGR bear a similar risk for neurocognitive and behavioral impairment, and the here defined modular network alterations confirm similar structural changes both by IUGR and EP at school age compared to control. Interestingly, the combination of both conditions (IUGR + EP) does not result in a worse outcome. In such cases, the alteration in network topology appears mainly driven by the effect of extreme prematurity, suggesting that these brain network alterations present at school age have their origin in a common critical period, both for intrauterine and extrauterine adverse conditions.

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

Nearest neighbor and validity-based clustering

The clustering problem can be formulated as the problem to find the number of clusters and a partition matrix from a given data set using the iterative or non-iterative algorithms. The author proposes a nearest neighbor and validity-based clustering algorithm where each data point in the data set is linked with the nearest neighbor data point to form initial clusters and then a cluster in the initial clusters is linked with the nearest neighbor cluster to form a new cluster. The linking between clusters is continued until no more linking is possible. An optimal set of clusters is identified by using the conventional cluster validity index. Experimental results on well-known data sets are provided to show the effectiveness of the proposed clustering algorithm.