Modified K-means Algorithm Research Articles

A Genetic Algorithm with Quantum Random Number Generator for Solving the Pollution-Routing Problem in Sustainable Logistics Management

The increase of greenhouse gases emission, global warming, and even climate change is an ongoing issue. Sustainable logistics and distribution management can help reduce greenhouse gases emission and lighten its influence against our living environment. Quantum computing has become more and more popular in recent years for advancing artificial intelligence into the next generation. Hence, we apply quantum random number generator to provide true random numbers for the genetic algorithm to solve the pollution-routing problems (PRPs) in sustainable logistics management in this paper. The objective of the PRPs is to minimize carbon dioxide emissions, following one of the seventeen sustainable development goals set by the United Nations. We developed a two-phase hybrid model combining a modified k-means algorithm as a clustering method and a genetic algorithm with quantum random number generator as an optimization engine to solve the PRPs aiming to minimize the pollution produced by trucks traveling along delivery routes. We also compared the computation performance with another hybrid model by using a different optimization engine, i.e., the tabu search algorithm. From the experimental results, we found that both hybrid models can provide good solution quality for CO2 emission minimization for 29 PRPs out of a total of 30 instances (30 runs each for all problems).

Strategy of Multi-Beam Spot Allocation for GEO Data Relay Satellite Based on Modified K-Means Algorithm

With the booming development of satellite applications, the giant constellations of low Earth orbit (LEO) satellites have introduced challenges for the data relay service. The multi-beam satellite not only offers concurrent access to a large number of objects, but can also meet the high data requirements toward specific coverage of the LEO constellation. However, the multi-beam satellite often faces the mismatch problem of spot allocation and data requirements, which can cause an overload traffic jam or a waste of resources. An optimization algorithm on spot beam allocation is necessary to automatically place the spot centers with appropriate beam widths in line with the density of the traffic demands and to realize the uniformity of the beam occupation. Compared with the conventional K-means algorithm, two adjustable parameters α and β are introduced: one for tuning the ratio of two components making up the distance matrix, and the other for setting the obligatory minimum number of objects per beam. In this paper, the whole process of the proposed method is demonstrated, including the establishment of the low-orbit satellite constellation model, the extraction of the distribution features, and the implementation and evaluation of the modified K-means algorithm. The results prove the validity of the proposed algorithm. A larger value of β with a relative smaller value of α tends to obtain the uniformity of beam occupation; the minimum standard deviation of objects per beam is achieved when α is 0.2 and β is 0.8. This demonstrates that the uniformity of objects per beam can be realized by adjusting the parameters of the distance determination matrix and the obligatory minimal number of objects in each beam. The impact of parameter range on the results is also analyzed.

The Use of Modified K-Means Algorithm to Enhance the Performance of Support Vector Machine in Classifying Breast Cancer

The Use of Modified K-Means Algorithm to Enhance the Performance of Support Vector Machine in Classifying Breast Cancer

Radar Jamming Effect Analysis Based on Bayesian Inference Network With Adaptive Clustering

Radar jamming effect analysis (RJEA) is important for improving the performance of radar sensors in jamming environments and one of the most popular methods for RJEA is Bayesian inference network (BIN). Notice the features in RJEA are usually continuous, so they should be discretized for BIN, where the feature discretization approach determines the node sizes and the discretization error, which further seriously affect the training time and inference precision of BIN, respectively. In this paper, we put forward a adaptive clustering approach for the features in RJEA to reduce the node sizes of BIN in the premise of keeping or even improving its inference precision. To do so, a modified K-means algorithm is designed, where the initial clustering centers are preset according to the density of samples, and the number of final clustering centers, not given in advance, is estimated with their locations simultaneously via iterative optimization. Finally, the performance of BIN with the adaptive clustering is validated in the experiments on pulse Doppler radar sensor with radial frequency noise, where 20 features are extracted for RJEA. Compared with uniform discretization, the proposed method can reduce the training time of BIN from 19.52 second to 4.39 second and improve its inference precision from 71 % to 82% simultaneously. Compared with K-means discretization, the proposed method results in similar inference precision but less training time of BIN.

Flexible Virtual Cell Design for Ultradense Networks: A Machine Learning Approach

With the ever-growing demand for even higher throughput, ultradense networks (UDNs) are being deployed for the fifth generation (5G) mobile communications. Although massively distributed radio access points (APs) result in a considerable increase in throughput, they also cause some critical problems. When employing a wireless backhaul, the backhaul capacity becomes a limiting factor, which may result in a high packet loss rate. Furthermore, dense deployment of APs leads to more frequent handoffs for mobile user equipments, which results in heavy measurement and signaling overhead. To address the problem of frequent handoffs, virtual cell (VC) has been considered as a promising solution. However, the limited wireless backhaul capacity encountered by inflexible VC design may still result in an intolerable packet loss rate. For a better trade-off between the packet loss rate and the handoff overhead, a machine learning approach for flexible VC design is proposed that leverages particle swarm optimization (PSO) to quickly find the optimal VC solution. To be responsive to the dynamic traffic demand and backhaul capacity of APs, a new parameter called “weighted distance” is employed in the modified K-means algorithm, which is nested in the PSO procedures for master AP selection and VC boundary determination. Compared with an exhaustive search, optimal VC solutions can be found efficiently through considerably fewer iterations. The proposed method is generic and applicable to disparate UDN application scenarios.

МЕТОДИКА СИНТЕЗА ПОДСИСТЕМЫ ИНТЕЛЛЕКТУАЛЬНОГО МОНИТОРИНГА ИНФОРМАЦИОННО}ТЕЛЕКОММУНИКАЦИОННОЙ СЕТИ СИТУАЦИОННОГО ЦЕНТРА

Introduction: in accordance with the task of forming a system of distributed situational centers of public authorities, it is necessary to interface heterogeneous segments of the department's information and telecommunications networks into a geographically distributed infrastructure with the construction of a subsystem for monitoring the state of its elements on it. The purpose of the study: based on the use of basic concepts of structural synthesis methods, structural analysis of network infrastructures, as well as parametric synthesis procedures, to develop a methodology for the synthesis of a new generation monitoring subsystem based on intelligent methods for identifying the state of the network. Results: the structuring of the controlled space by the terms "monitoring zone"," critical element"," state classes "form the basis of new methods of intelligent monitoring that are" insensitive " to the properties of the constant evolution of network infrastructures. The proposed method of synthesis of the intelligent monitoring subsystem consists of sequentially performed stages of structural synthesis, parametric synthesis and structural analysis of the network. Practical significance: the successive stages of the methodology using graph distance measurement procedures and the modified k-means algorithm allow not only to identify the type of network state, but also to reasonably, using instrumental calculation methods, present in the interests of the decision support system sets of acceptable values of the main parameters and probabilistic-temporal characteristics of the monitoring subsystem for subsequent reconfiguration of the network and preventing its transition to an inoperable state. Discussion: the modification of the k-means algorithm proposed in the study differs in that in the classical algorithm, work is carried out on points of Euclidean space, and in the proposed method we are talking about a graph space with metrics in the form of graph distances, while data clouds are used as initial data for classification in the k-means algorithm as disordered data sets that are not tied to any of the measurement scales, and in the proposed algorithm, the data cloud is represented by a set of graphs in a given topological space of graph metrics, describing the state of the network in time.

A dynamic replicative K-means with self-compiling particle swarm intelligence for dataset classification

The classification techniques proposed so far is not sufficiently intelligent in classifying data set beyond two level classifications. To multi classify the data set for network data we are in need of more hybrid algorithms. In this paper we propose a hybrid technique by combining a modified K-means algorithm called dynamic replicative K-means (DRKM) with self-compiling particle swarm intelligence (SCPSI). The dataset we have chosen for the experiment is KDD Cup 99. The DRKM-SCPSI performs better in terms of detection rate (DR), false positive rate (FPR) and accuracy which is visible from the results presented.

A Review on Data Discrepancy Factor Performance for Industrial Applications using Clustering Algorithms

DDF is the most significant measure among different bunch execution procedures to assess the immaculateness of any group component. Ordinarily, best groups are assessing by processing the quantity of information focuses inside a bunch. At the point when this tally is comparable to the quantity of required information focuses then this group is viewed as great. The greatness of the bunch system is fundamental not exclusively to discover the information check inside a group yet in addition to inspect it by totalling the information focuses these are (I) present inside a group where it ought not be and the other way around and (ii) not grouped for example anomalies (OL). The principle usefulness of DDF is that all bunch focuses can be gathered in comparative groups without exceptions, the current paper features on how contrasted with DDF more effective Clusters can be shaped through the Modern DDF. Further, we assess the exhibition of some grouping calculations, K-Means. As of late we, fostered the Modified K-Means Algorithm and Hierarchical Algorithm by utilizing the Data Discrepancy Factor (DDF).

A New Hybrid Routing Protocol Using a Modified K-Means Clustering Algorithm and Continuous Hopfield Network for VANET

Vehicular Ad-hoc Networks (VANET) offer several user applications for passengers and drivers, as well as security and internet access applications. To ensure efficient data transmission between vehicles, a reliable routing protocol is considered a significant challenge. This paper suggests a new clustering-based routing protocol combining a modified K-Means algorithm with Continuous Hopfield Network and Maximum Stable Set Problem (KMRP) for VANET. In this way, the basic input parameters of the K-Means algorithm, such as the number of clusters and the initial cluster heads, will not be selected randomly, but using Maximum Stable Set Problem and Continuous Hopfield Network. Then the assignment of vehicles to clusters will be carried out according to Link Reliability Model as a metric that replaces the distance parameter in the K-Means algorithm. Finally, the cluster head is selected by weight function according to the amount of free buffer space, the speed, and the node degree. The simulation results have proved that the designed protocol performs better in a highway vehicular environment, compared to the most recent schemes designed for the same objective. In fact, KMRP reduces traffic congestion, and thus provides a significant increase in Throughput. In addition, KMRP decreases the transmission delay and guarantees the stability of the clusters in high density and mobility, which acts better in terms of the Packet Delivery Ratio.

Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering

Clustering is a technique in data mining thatgroups data sets into similar data clusters. One of thealgorithms that is commonly used for clustering is K-Means.However, the K-Means algorithm has several weaknesses, oneof them is the random factor in initial centroid selection, sothat cluster result is inconsistent even though it is tested withthe exact same data. The Modified K-Means algorithm focuseson selecting the initial centroid to overcome inconsistencies ofcluster results in the K-Means method. The test was conductedusing sentipol dataset and only focused on comment data.Furthermore, the specified number of clusters is 3 based on thenumber of existing comment labels (positive, negative, andneutral). According to testing result proves that Modified KMeans algorithm produces better purity value than K-Meansalgorithm. Modified K-Means algorithm produces average ofpurity value 0,42, while K-Means produces average of purityvalue 0,391. Meanwhile, from testing related to random factorsconducted 5 times with the same attributes and test data, theresults of the cluster on the Modified K-Means algorithm didnot change, so automatically the resulting purity value was alsothe same. Whereas in the K-Means algorithm, the clusterresults always change in each test, so the result of purity valueis also likely to change.

A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation

Non-system errors that occur during data entry or data collection create noisy data that reduce the success of classification systems. To eliminate this data, a classification system with a new data reduction method consisting of a modified k-means algorithm using relief algorithm coefficients named MKMA-RAC was developed. The main theme of this article is the elimination of noisy data and its consistent application to the classification system using the k-fold cross-validation method. By means of the developed system, the training data became free from noisy data by integrating the support vector machine, linear discriminant analysis (LDA) and decision tree classifiers with MKMA-RAC-based data reduction for every fold. The data reduction process was not applied for the test data. Datasets used in the proposed method were the Hepatitis, Liver Disorders, SPECT images and Statlog (Heart) dataset taken from the UCI database. Classification performance values obtained both from the proposed method and without the proposed method with tenfold CV were given for these datasets. For Hepatitis, Liver Disorders, SPECT images and Statlog (Heart) datasets, and classification successes of the proposed system with SVM classifier were 96.88%, 74.56%, 87.24%, and 90.00%, classification successes of the proposed system with LDA classifier were 94.91%, 69.05%, 82.38%, and 88.52%, classification successes of the proposed system with decision tree classifier were 96.25%, 77.73%, 88.77% and 89.63%, respectively. The test results have shown that the proposed system generally achieved higher classification performance than other literature results. Therefore, the performance is very encouraging for pattern recognition applications.

Flexibility Evaluation Method of Power Systems with High Proportion Renewable Energy Based on Typical Operation Scenarios

The development of renewable energy represented by wind, photovoltaic and hydropower has increased the uncertainty of power systems. In order to ensure the flexible operation of power systems with a high proportion of renewable energy, it is necessary to establish a multi-scenario power system flexibility evaluation method. First, this study uses a modified k-means algorithm to cluster operating scenarios of renewable energy and load to obtain several typical scenarios. Then, flexibility evaluation indices are proposed from three perspectives, including supply and demand balance of the zone, power flow distribution of the zone and transmission capacity between zones. Next, to calculate the flexibility evaluation indices of each scenario—and according to the occurrence probability of each scenario—we multiplied the indices of each scenario by the scenario occurrence probability to obtain comprehensive evaluation indices of all scenarios. Based on the actual historical output data of renewable energy and load of a southern power system in China, a flexibility evaluation was performed on the modified IEEE 14 system and modified IEEE 39 system. The results show that the proposed clustering method and flexibility indices can effectively reflect the flexibility status of the power system.

Segmentation of Lungs in Chest Radiographs using Optimized Clustering Technique

Lung Cancer is considered the most common type of disease, when compared to that of the other types. It is one of the leading types of cancer that causes majority of the patient death worldwide. It is often detected only at the later stage as they are usually diagnosed at their advanced stages. Survival of patients with lung cancer is almost impossible. They often die within one year after the onset of clinical symptoms. Screening and early detection play an important role in saving the life of a patient. Chest radiography and computerized tomography scans are the techniques mostly used to diagnose and detect tumor in lungs. They require less radiation dose, and is available in most of the diagnostic centers. Their cost is also less when compared to the other techniques used for diagnosis. Nodule detection by using conventional radiographs is still not much effective, so there arises a need for alternative image processing techniques to improve the efficiency of detection. Image segmentation is considered as the first step in processing an image. Further analysis of the image would be made more effective if segmentation is efficient. There exits many segmentation algorithms based on clustering and thresholding approaches. In this paper, a bimodal, optimized and modified k-means algorithm is developed to segment the chest images.

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Learning Management System (LMS) is well de-signed and operated by an exceptional teaching team, but LMS does not consider the needs and characteristics of each student’s learning style. The LMS has not yet provided a feature to detect student diversity, but LMS has a track record of student learning activities known as log files. This study proposes a detection model of student’s learning styles by utilizing information on log file data consisting of four processes. The first process is pre-processing to get 29 features that are used as the input in the clustering process. The second process is clustering using a modified K-Means algorithm to get a label from each test data set before the classification process is carried out. The third process is detecting learning styles from each data set using the Naive Bayesian classification algorithm, and finally, the analysis of the performance of the proposed model. The test results using the validity value of the Davies-Bouldin Index (DBI) matrix indicate that the modified K-Means algorithm achieved 2.54 DBI, higher than that of original K-Means with 2.39 DBI. Besides having high validity, it also makes the algorithm more stable than the original K-Means algorithm because the labels of each dataset do not change. The improved performance of the clustering algorithm also increases the values of precision, recall, and accuracy of the automatic learning style detection model proposed in this study. The average precision value rises from 65.42% to 71.09%, the value of recall increases from 72.09% to 80.23%, and the value of accuracy increases from 67.06% to 71.60%.

K-RMS Algorithm

Clustering is an unsupervised learning problem in the domain of machine learning and data science, where information about data instances may or may not be given. K-Means algorithm is one such clustering algorithms, the use of which is widespread. But, at the same time K-Means suffers from a few disadvantages such as low accuracy and high number of iterations. In order to rectify such problems, a modified K-Means algorithm has been demonstrated, named as K-RMS clustering algorithm in the present work. The modifications have been done so that the accuracy increases albeit with less number of iterations and specially performs well for decimal data compared to K-Means. The modified algorithm has been tested on 12 datasets obtained from UCI web archive, and the results gathered are very promising.

A Data Clustering Based Probabilistic Power Flow Method for AC/VSC-MTDC

This paper investigates the role of modified clustering methods to account correlated uncertainties in the probabilistic power flow problem for alternate current/voltage source control-multiple terminal direct current (ac/VSC-MTdc). Such uncertainties are introduced by stochastic renewable generation units or variable loads. The wind farm is a type of stochastic generation units that grows dramatically in power networks. Due to the increase of penetration level of renewable generations, number of uncertainty sources is increased. Clustering methods are suitable tools for handling stochastic power system problems, because these methods provide acceptable results with less computation in comparison to other common methods such as Monte Carlo simulations. In this paper, two modified methods are used for clustering uncertainty sets. These methods include modified K-means algorithm, and modified self-organized map neural network. After clustering, the power flow of ac/VSC-MTdc is carried out for cluster centers of each method. Results of the probabilistic power flow for case study that is based on the IEEE 24-bus and IEEE 118-bus test systems are presented and compared with different clustering techniques, Monte Carlo simulation, and Latin hypercube sampling (LHS) methods. Such results show that power flow calculation with clustering methods is more accurate than LHS method and is faster than Monte Carlo simulation method.

Learning-Based User Clustering and Link Allocation for Content Recommendation Based on D2D Multicast Communications

Content recommendation based on device-to-device (D2D) multicast communications is expected to become a promising approach to improve local area services. Importantly, two main challenges should be considered: i) user clustering—in order to be tailored to recommend contents, the members in the same cluster should have great similarity in multiple characteristics, and ii) link allocation—we should use as little resource consumption and information exchange as possible while keeping the recommendation accuracy. In this paper, we firstly quantify the degree of the similarity between two target users with regard to multiple characteristics. Guided by such similarity, we define a clustering validity index in terms of between-within proportion (BWP) to characterize the clustering performance. Then, the issue of user clustering is modeled as a sum BWP maximum problem, and a user clustering algorithm based on modified K-means algorithm is designed to solve it in a fast-operating and low-complexity way. After user clustering, we model the issue of link allocation as a weighted aggregate interference minimization problem, and then transform it to an exact potential game. As such, a link allocation algorithm based on stochastic learning algorithm is proposed which helps to obtain the result of this game in a distributed way without complete information. Also, we analyze its convergence and optimality performance. Simulation results demonstrate the effectiveness of our proposed algorithms.

Analysis of Students Performance using Modified K-Means Algorithm (Machine Learning Techniques)

Analysis of Students Performance using Modified K-Means Algorithm (Machine Learning Techniques)

Automatic Brain Tissue Segmentation using Modified K-Means Algorithm Based on Image Processing Techniques

Brain tumor, due to uncontrolled development of abnormal cells, is one of the hazardous illnesses that happen in the brain. A fully automatic brain tissue segmentation using improved k means segmentation is discussed in this paper. Generally the brain tumor tissue can appear at any location at different size and shapes. Manual brain tumor detection is not only time-consuming, it is also linked to human errors and depends on the expertise and experience of a medical pathologist. Automatic detection is required in a computer-aided detection system (CAD) for medical images such as MRI. This automatic detection includes pre-processing, segmentation and medical image classification. The preprocessing techniques eliminate noise. Separate the region of interest from the background picture using the segmentation methods. Finally, the classification is conducted to identify brain tumor automatically. The outcomes are also compared between the suggested method and the current methods.

Doubling Phase Shifters for Efficient Hybrid Precoder Design in Millimeter-Wave Communication Systems

Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave (mmWave) communications, but its precoder design is highly complicated. In this paper, we propose a new hybrid precoder implementation, namely the double phase shifter (DPS) implementation, which enables highly tractable hybrid precoder design. Efficient algorithms are then developed for two popular hybrid precoder structures, i.e., the fully- and partially-connected structures. For the fully-connected one, the RF-only pre-coding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator problem and a low-rank matrix approximation problem, respectively. In this way, computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency (RF) chains. On the other hand, the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem. To enhance the performance of this cost-effective structure, dynamic mapping from RF chains to antennas is further proposed, for which a greedy algorithm and a modified K-means algorithm are developed. Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones. It shows that, with the proposed DPS implementation, the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.