Existing K-means Algorithm Research Articles

Optimization of Ideological and Political Education Strategies in Colleges and Universities Based on Deep Learning.

In the current technological world, artificially intelligent deep learning techniques are adapted in many fields. This advanced technology is also used in the field of education. In this study, people will conduct research on the optimization of ideological and political education strategies in colleges and universities based on deep learning. Deep learning is often a machine learning technique that uses artificial neural networks that allow a machine to imitate human behaviour. Ideological and political education deals with the social studies implied by the political scenario. Ideological and political education aims to teach the younger generation social, economic, and political awareness. In our proposed system, people will deploy the deep learning algorithm named brute force algorithm to optimize ideological and political education in colleges and universities. The teaching optimization is performed by automating the training of the deep learning model. The results were compared with the existing K-means algorithm, and it is observed that the proposed system has achieved a higher accuracy of 99.12% in optimizing the educational strategies.

Intelligent Analysis and Classification of Piano Music Gestures with Multimodal Recordings.

In the traditional recording system, recording any music includes a sizeable instrumental setup and allocates space for the music players. Lighter and fewer devices are replacing larger instruments due to technological advancement and epidemic environmental conditions. This research focuses on text, but audio and video types are also considered. Multiple signal classification with a 5G-based wireless communication network algorithm is implemented to perform the automatic recording and classification of the music data. In this research, a multi-modal gesture recognition dataset is considered for analysis. The dataset was obtained using sensor networks and an intelligent system to record the musical gestures and classify the recorded gestures. The development of machine learning algorithms is not limited to similar technological concepts. Still, it extends to almost all other technical resources such as the 5G network, signal processing, networking, and all other technical resources. This would lead to additional engineering challenges that are utilized in most cases, such as the development of gestures with multi-mode recording. This research has proposed MSA with WCN algorithm to perform intelligent analysis and classification of piano music gestures and is compared with the existing K-Means algorithm and achieved an accuracy of 99.12%.

Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering

Most natural <span>world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median to identify overlapping clusters in the presence of outliers. This new method aims to determine the insensitivity of the OCKMEx algorithm in locating data points that overlap even with outliers. An experimental evaluation of the algorithm was conducted wherein synthetic datasets served as a data source, and the F1 measure criterion was applied to assess the OCKMEx algorithm performance. Results indicate that the OCKMEx algorithm implementing the use of k-median performed a higher accuracy rate of 100% in identifying data points that overlap even with outliers compared to the existing k-means algorithm. The algorithm exhibited a promising performance in identifying overlapping clusters and was resistant to outliers.</span>

A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids

The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost.

Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.

Augmented Affinity Propagation-Based Network Partitioning for Multiple Controllers Placement in Software Defined Networks

Software Defined Networks (SDN) divide network intelligence and packet forwarding functionalities between control plane and data plane devices respectively. Multiple controllers need to be deployed in the control plane in large SDN networks to improve performance and scalability. In a multi-controller scenario, finding the adequate number of controllers and their load distribution are open research challenges. In a large-scale network, the control plane load balancing is termed a controller placement problem (CPP). It is observed that of the existing solutions for the CPP, clustering-based approaches provide computationally less intensive solutions. The proposed augmented affinity propagation (augmented-AP) clustering identifies the required number of network partitions and places the controllers such that the distribution of switches to the controller is much better than with existing algorithms. The simulation results show that the computed controller imbalance factor of augmented-AP algorithm outperforms the existing k-means algorithm.

An enlarged map-reduce using 2logmean-PSO optimization for unstructured data

Text clustering system is a proper technique which mainly segments large measure of textual documents into clusters. The size of the material influences the clustering of text by reducing its performance. In this manner, the textual document comprises sparse and uninformative features, and thus raises the computational time and decreases the execution of primary clustering process. Feature selection is a crucial system to choose another subset of instructive text feature to enhance text clustering execution and diminish computational time. The implemented model proposes a 2logmean-particle swarm optimization algorithm for the unstructured text clustering. In this newly proposed technique, all the texts are initially converted into ASCII value, and then by using the particle swarm optimization, the document text is clustered. The outcomes display that clustering accuracy of the implemented method is high compared to the existing K-means algorithm. Furthermore, performances of newly implemented techniques are evaluated concerning scalability, less computation speed with colossal dimensionality reduction.

Segmentation of High Resolution Worldview-2 Satellite Images

This paper presents the segmentation technique used to segment the Worldview-2 high resolution satellite multispectral (MS) images. First the spectral features like Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Modified Soil Adjusted Vegetation Index (MSAVI) are considered to extract the spectral features from the MS image. Next the MS image is segmented by using the over segmented k-means algorithm with novel initialization (OSKNI) method. The proposed method performs well in terms of User’s accuracy (UA), Producer’s accuracy (PA) and overall segmentation accuracy (OVA) compared to the existing k-means algorithm.

An incremental approach for solution of text clustering problem

Text clustering is a significant study field in data mining. In text clustering, hidden-information is revealed by analysing text datasets with data mining techniques. Text clustering is used in many current application areas such as document recognition, document organisation, indexing, and visualisation. Since it is used in important applications it is in need of efficient algorithms. In this paper, k-means based algorithms in literature are investigated and a novel k-means based text clustering algorithm is defined. Proposed algorithm is compared with existing k-means algorithm in literature on five real-world datasets. Obtained implications show that recommended algorithm is efficient and useful.

Designing a New Hybrid K-Means Optimization Algorithm

Abstract- Clustering is an unsupervised learning process with the main objective of organizing data into certain clusters and groups such that the data objects in the same cluster have higher similarity but dissimilar to the objects in other clusters. Many algorithms have been presented till now. These algorithms are k-means algorithm, hybrid k-means algorithms, and variants of k-means. The hybrid k-means optimization algorithms are those that combines k-means algorithm with various optimization algorithms like KBAT, CGA, KFA etc. The k-means algorithms have many problems like k-means algorithm converges to local minima rather than giving a global optimum results, hybrid k-mean algorithms have accuracy and efficiency problems. A new hybrid k-means optimization clustering algorithm is presented in the proposed study to solve the problems of existing k-means algorithm and hybrid k-mean algorithms. The output parameters like intra cluster distance, purity, recall etc are use to compare the performance of existing hybrid k-means algorithm with a new hybrid k-means optimization clustering algorithm. The results of these output parameters show that the proposed ABCGA technique is more efficient and it requires less computational time than existing hybrid k-means algorithm. Also the recall and purity of proposed hybrid algorithm is better than existing hybrid k-means algorithms and k-means algorithm but still there is scope for improvement in the proposed technique to extend this approach to handle variety of situations and information.

Dynamic Recommendation System Using Enhanced K-means Clustering Algorithm for E-commerce

E-commerce organizations are growing day by day over time in terms of both business and data. Maximum organizations rely on these e-commerce websites to attract new customers and maintain existing ones. Dynamic recommendation system can be used to achieve this goal. It works towards improving the result factors of product priority displayed over the users search records. . This paper focuses on providing real-time dynamic recommendations to all registered users of the website. Here the dynamic recommendation technology is proposed, which uses enhanced K-Means Algorithm to generate item recommendation. By compiling the real time e-commerce data and comparing the system with existing K-means algorithm, the effectiveness of the proposed system is evaluated. The results prove that the proposed system provides good quality, accuracy and reduces the limitations of the conventional recommendation system. The experimental evaluation is measured on precision, recall and accuracy for proving the robustness of the system.

A Study on Efficient Memory Management Using Machine Learning Algorithm

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

A Novel Approach for Data Clustering using Improved K-means Algorithm

statistic and data mining, k-means is well known for its efficiency in clustering large data sets. The aim is to group data points into clusters such that similar items are lumped together in the same cluster. The K-means clustering algorithm is most commonly used algorithms for clustering analysis. The existing K-means algorithm is, inefficient while working on large data and improving the algorithm remains a problem. However, there exist some flaws in classical K-means clustering algorithm. According to the method, the algorithm is sensitive to selecting initial Centroid. The quality of the resulting clusters heavily depends on the selection of initial centroids. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In the proposed project performing data clustering efficiently by decreasing the time of generating cluster. In this project, our aim is to improve the performance using normalization and initial centroid selection techniques in already existing algorithm. The experimental result shows that, the proposed algorithm can overcome shortcomings of the K-means algorithm. KeywordsAnalysis, Clustering, k-means Algorithm, Improved k- means Algorithm

A PROPOSED ALGORITHM FOR DETERMINING THE OPTIMAL NUMBER OF CLUSTERS

Data clustering is a data exploration technique that allows objects with similar characteristics to be grouped together in order to facilitate their further processing. The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and an improvement of the existing k-means algorithm to assist the selection is proposed. The paper concludes with an analysis of the results of using cluster validation referring to some measures that are classified as internal and external indexes to determine the optimal number of clusters for the K-means algorithm. There are applied some stopping criterion referring to those indexes for evaluating a clustering against a gold standart.

A Comprehensive Survey on Centroid Selection Strategies for Distributed K-means Clustering Algorithm

xtremely large data sets often known as Big Data are analyzed for interesting patterns, trends, and associations, especially those relating to human behavior and interactions. Extraction of meaningful and useful information needs to be done in parallel using advanced clustering algorithms. In this paper, effort has been made to tweak in changes to the existing K-means algorithm so as to work in parallel using MapReduce paradigm. K-means due to its gradient descent nature is highly sensitive to the initial placement of the cluster centers. This random initialization of cluster centers results in empty clusters and slower convergence. In this paper, an overview of existing methods with emphasis on computational efficiency is presented. Comparison of three well known linear time complexity initialization methods has been presented here. These methods are analyzed on two different data sets. The experimental results are recorded and presented with insights on different initialization methods for practitioners.

A novel method of online trading customer classification

Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the shortages of the existing K-means algorithm of data-mining for customer classification, this paper advances a new customer classification algorithm through improving the existing K-means algorithm. Firstly, the paper designs 21 customer classification indicators based on consumer characteristics and behaviors analysis, including customer characteristics type variables and customer behaviors type variables; then, limitation of K-means algorithm is analyzed; following, corresponding improvements for K-means algorithm are advanced including improvement of K-means algorithm principle, improvement of initial classification centers selection and improvement of the flow of K-means algorithm. Finally, the experimental results verify that the new algorithm can improve effectiveness and validity of customer classification when used for classifying network trading customers practically. Key words: Electronic marketing, customer classification, K-means algorithm, consumer characteristics analysis, customer behaviors analysis.