Clustering Algorithm For Classification Research Articles

A Comparative Study of Students Graduation Analysis Using Classification Methods in Undergraduate Electrical Engineering Tidar University

This research aimed to classify achievement factors for electrical engineering students at Tidar University using K-Means and Agglomerative Clustering classification algorithms. The goal was to understand if any parameters influence high-achieving student performance. The Indonesian government and private sector for university students provide significant education funds. Student scholarships are awarded based primarily on GPA and entry path, overburdening staff and causing confusion during distribution to eligible recipients. A system was needed to accommodate additional eligible criteria. The researcher selected factors to identify engineering student performance, including school origin, entry path, tuition fees, and GPA. These inputs could determine graduation status. The results compared calculation methods based on collected data accuracy, processing times, and characterizing clustered data to determine the best classification method. Agglomerative Hierarchical Clustering performed better. Accuracy testing on 600 training data points yielded 73.94% for improved K-means and 90.42% for AHC. The Average processing time was 674.92 seconds for improved K-means and 554.35 seconds for AHC. Silhouette testing also characterized calculation methods, with improved K-means scoring best at 0.654 and AHC at 0.787 using two clusters.

Building a Korean Language Teaching Resource Library Based on Python Crawler

Abstract Nowadays, with the popularization of online education how to efficiently and accurately obtain the required educational resources from web pages is one of the key concerns in the teaching process. Based on the principle of maximum distance, the study improves the K-mean and establishes a learner group feature model based on the DM-K-mean clustering algorithm. In addition, for the problem that the importance of learning resources to learners changes with time, this paper integrates the time information into the neural collaborative filtering algorithm through the clustering classification algorithm. It proposes a deep learning-based recommendation algorithm for Korean language teaching resources. Python crawler technology is used to obtain relevant experimental data from the online teaching platform to verify the performance of the proposed model, so as to construct the Korean language teaching resource base. The learner group characteristic model classifies the sample students into three categories: excellent (0.489), good (0.307), and average (0.204) learning situations. The HR and NDCG values of the Improved NeuMF resource recommendation model have been improved by 3.6% and 2.2% compared to the NeuCF model, respectively, and the performance is optimal under various factors. The proposed system for teaching Korean language resources in this paper can recommend resources based on learner profiles to help learners access teaching resources and improve efficiency.

Combined Application of Partition Clustering Classification and Gerchberg-Papoulis Optimization Algorithm for Spherical Near Field Antenna Measurements

An adaptive sampling and optimized extrapolation scheme for spherical near-field antenna testing is proposed. The method relies on the partition clustering classification algorithm and Voronoi classification to divide a small amount of initial data into subclasses and cells. The sampling density and rates of variation between adjacent sampling points are used as an overall metric function to evaluate the sampling dynamics at each location. Appropriate interpolation is performed in the highly dynamic region to increase the effective data in the near-field samples. The Gerchberg-Papoulis algorithm extrapolates the unnecessary interpolation region to improve the near-field sampling accuracy. This method uses a small amount of initial near-field sampled data for near-far field conversion to achieve the same precision as uniform oversampling. The feasibility and stability of the algorithm are proved from the actual measurement results.

Machine Learning Based Hybrid Intrusion Detection ForVirtualized Infrastructures In Cloud Computing Environments

Nowadays technology steady shift have seen from the models of conventional software to the cloud technologies. Cloud computing is rapidly becoming the standard by fulfilling the computer infrastructure demands of all sizes of enterprises. One of the essential tools forbuilding trustworthy& secure environment of Cloud computing is the Intrusion detection, given the ubiquitous cyber attacks which can proliferate morph dynamically & rapidly. Machine learning (ML) based hybrid intrusion detection for virtualized infrastructures in cloud computing environments is presented in this paper. This infrastructure uses Hybrid algorithm: SVM (support vector machine) & K – means clustering classification algorithm, for improving the anomaly detection system accuracy. For evaluating this approach, UNSW-NB15 study is utilized from dataset & results compared with earlier techniques. For evaluating theperformance of suggested technique utilizes performance measures like average detection time. This approach has better accuracy compared to earlier approaches.

Implementation of address recording management using the K-Means clustering classification algorithm in Kebayoran District, DKI Jakarta

The area is the center of problems in the administrative record management of Kebayoran District, because of its dense condition and it is difficult to determine land measurements due to the density of residential areas. The problem in Indonesia to this day is that the administrative boundaries of the kelurahan already exist, but the administrative boundaries for the Rukun Warga / Rukun Tetangga (RW / RT) do not yet exist. The local government of DKI already has a large scale map (1: 1,000) to map RW administrative boundaries. Large-scale mapping (Batas RW) is useful for accurate information on incidence of dengue fever or other diseases, thereby eliminating information bias due to the use of village boundary maps. Another benefit is the accuracy of address management for customers, for example PDAM customers, to facilitate verification of customer data with large-scale maps, especially those that only include RT / RW addresses, without mentioning street names and household numbers. The method used is data mining K-Means Clustering. By using this method, the data that has been obtained can be grouped into several clusters, where the application of the KMeans Clustering process uses Excel calculations. The processed data is divided into 3 clusters, namely: high cluster (C1), medium cluster (C2) and low cluster (C3). The iteration process of this research occurs 2 times so that an assessment is obtained in classifying the household / neighborhood unit based on the Kelurahan. The results obtained are that there is 1 neighborhood unit with the highest cluster (C1), there are 4 neighborhood units with 4 medium clusters (C2), and 5 neighborhood units with the lowest cluster (C3). This data can be input to the sub-district to disseminate information about dengue fever, health education, and for the accuracy of PDAM customer address management and others.

Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course.

This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

Edge Sensing‐Enabled Multistage Hierarchical Clustering Deredundancy Algorithm in WSNs

Due to the defects caused by limited energy, storage capacity, and computing ability, the increasing amount of sensing data has become a challenge in wireless sensor networks (WSNs). To decrease the additional power consumption and extend the lifetime of a WSN, a multistage hierarchical clustering deredundancy algorithm is proposed. In the first stage, a dual‐metric distance is employed, and redundant nodes are preliminarily identified by the improved k‐means algorithm to obtain clusters of similar nodes. Then, a Gaussian hybrid clustering classification algorithm is presented to implement data similarity clustering for edge sensing data in the second stage. In the third stage, the clustered sensing data is randomly weighted to deduplicate the spatial correlation data. Detailed experimental results show that, compared with the existing schemes, the proposed deredundancy algorithm can achieve better performance in terms of redundant data ratio, energy consumption, and network lifetime.

Feature Extraction of Partial Discharges During Multiple Simultaneous Defects in Low-Voltage Electric Machines

Partial discharge (PD) defects are initiated in the electrical machines over different life stages. The intensity of PD defects increases continuously with time, which may lead to insulation failure. In some cases, multiple PD defects may occur concurrently leading to a faster equipment failure.This article presents a methodology for the separation of multiple PD defects in low-voltage motors. The characteristics of the recorded PD signals have been investigated to estimate the severity of these defects. The cumulative energy (CE) function in the time domain has been calculated from the PD signals and its significant features, which include amplitude, range, skewness, kurtosis, autocorrelation functions, cross correlation functions, and width parameter of CE signals, have been compared for the separation of various defects. Therefore, 6-D feature space consisting of peak value, dispersion, symmetry, sharpness, similarity, and shape features of CE functions has been produced for the separation of multiple defects in the motors. Finally, the K-mean clustering classification algorithm has been adopted using significant features of CE functions to discover their clusters in the feature space. The proposed algorithm has been validated based on the adjusted Rand index (ARI) function. Thus, it has been observed that the proposed procedure is effective for the separation of mixed PD signals from multiple defects.

Isomorphs in nanoconfined liquids.

We study in this paper the possible existence of Roskilde-simple liquids and their isomorphs in a rough-wall nanoconfinement. Isomorphs are curves in the thermodynamic phase diagram along which structure and dynamics are invariant in suitable nondimensionalized units. Two model liquids using molecular dynamics computer simulations are considered: the single-component Lennard-Jones (LJ) liquid and the Kob–Andersen binary LJ mixture, both of which in the bulk phases are known to have good isomorphs. Nanoconfinement is implemented by adopting a slit-pore geometry with fcc crystalline walls; this implies inhomogenous density profiles both parallel and perpendicular to the confining walls. Despite this fact and consistent with an earlier study [Ingebrigtsen et al., Phys. Rev. Lett., 2013, 111, 235901] we find that these two nanoconfined liquids have isomorphs to a good approximation. More specifically, we show good invariance along the isomorphs of inhomogenous density profiles, mean-square displacements, and higher-order structures probed using the topological cluster classification algorithm. Our study thus provides an alternative framework for understanding nanoconfined liquids.

Automated Labeling Process for Unknown Images in an open-world Scenario

Most of the recognition systems presume a controlled, well-defined research setting, where all possible classes that can appear during a test are known a priori. This environment is referred to as the ``closed-world'' model, while the ``open-world'' model implies that unknown classes can be incorporated into a recognition algorithm whilst being predicted. Therefore, recognition systems that operate in the real world have to deal with these unknown categories. Our objective was not only to detect data that originate from categories unseen during training, but to identify similarities between pieces of unknown data and then form new classes by automatically labeling them. Our Double Probability Model was extended by an image clustering algorithm, in which Kernel K-means was used. A new procedure, namely the Cluster Classification algorithm for the detection of unknowns and automated labeling, is proposed. These approaches facilitate the transition from open-set recognition to an open-world problem. The Fisher Vector (FV) was used for the mathematical representation of the images and then a Support Vector Machine introduced as a classifier. The measurement of similarity was based on the FV representations. Experiments were conducted on the Caltech101 and Caltech256 datasets of images and the Rand Index was evaluated over the unknown data. The results showed that our proposed Cluster Classification algorithm was able to yield almost the same Rand Index, even though the number of unknown categories increased.

基于特征熵权的集群分类算法

基于特征熵权的集群分类算法

Electricity clustering framework for automatic classification of customer loads

Abstract Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classification phase. The computation time of the proposed framework is less than that of previous classification techniques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.

Embedded vision equipment of industrial robot for inline detection of product errors by clustering–classification algorithms

The article deals with the design of embedded vision equipment of industrial robots for inline diagnosis of product error during manipulation process. The vision equipment can be attached to the end effector of robots or manipulators, and it provides an image snapshot of part surface before grasp, searches for error during manipulation, and separates products with error from the next operation of manufacturing. The new approach is a methodology based on machine teaching for the automated identification, localization, and diagnosis of systematic errors in products of high-volume production. To achieve this, we used two main data mining algorithms: clustering for accumulation of similar errors and classification methods for the prediction of any new error to proposed class. The presented methodology consists of three separate processing levels: image acquisition for fail parameterization, data clustering for categorizing errors to separate classes, and new pattern prediction with a proposed class model. We choose main representatives of clustering algorithms, for example, K-mean from quantization of vectors, fast library for approximate nearest neighbor from hierarchical clustering, and density-based spatial clustering of applications with noise from algorithm based on the density of the data. For machine learning, we selected six major algorithms of classification: support vector machines, normal Bayesian classifier, K-nearest neighbor, gradient boosted trees, random trees, and neural networks. The selected algorithms were compared for speed and reliability and tested on two platforms: desktop-based computer system and embedded system based on System on Chip (SoC) with vision equipment.

Data Mining Clustering Algorithm Research and Application

In this paper, first introduce data mining knowledge then focuses on the clustering analysis algorithms, including classification clustering algorithm, and each classification typical cluster analysis algorithms, including the formal description of each algorithm as well as the advantages and disadvantages of each algorithm also has a more detailed description. Then carefully introduce data mining algorithm on the basis of cluster analysis. And using cohesion based clustering algorithm with DBSCAN algorithm and clustering in consumer spending in two-dimensional space, 2,000 data points for each area, and get a reasonable clustering results, resulting in hierarchical clustering results valuable information, so as to realize the practical application of the algorithm and clustering analysis theory combined.

Classification and separation of partial discharge signals by means of their auto-correlation function evaluation

This paper describes a K-Means Clustering classification algorithm for the separation of Partial Discharge (PD) signals and pulsating noise due to multiple sources occurring in practical objects. It is based on the comparison of the Auto-Correlation Function (ACF) of the recorded signals assuming that the same source can generate signals having similar ACF while ACF differ when signals with different shapes are compared. The ACF has been selected for its capability of well summarize both time- and frequency-dependent features of the signals. A correlation index that presents the best compromise between strong and weak discrimination among pulses, has been selected out of different distance measurements. The final result of the algorithm is a set of classes containing signals having similar shape which can be processed successively for signal source identification. Meaningful applications of the proposed algorithm are also reported. Improvements in separation effectiveness can enhance the clearness of the PD patterns and, consequently, the quality of the defect identification.

High-Throughput Identification and Classification Algorithm for Leukemia Population Statistics

Early detection of leukemia and reduced risk to human health can result from interdisciplinary integration of image analysis with clinical experimental results. Image analysis relies on efficient and reliable processing algorithms to make quantitative judgments on image data. This article presents the design and implementation of an efficient and high-throughput leukemia cell count and cluster classification algorithm to automatically quantify leukemia population statistics in the field of view. The algorithm is divided into two stages: (1) the cell identification stage and (2) the cell classification and inspection stage. The cell identification stage accurately segments background and noise from foreground pixels. A boundary box is generated enclosing the foreground pixels identifying all isolated cells and cell clusters. The cell classification and inspection stage uses one-dimensional intensity profiles that behave as signature plots to segregate isolated cells from cell clusters and evaluate total count within each cluster. The designed algorithm is tested with a variety of leukemia cell images that vary in image acquisition conditions, image sizes, cell sizes, intensity distributions, and image quality. The proposed algorithm demonstrates good potential in processing both ideal and nonideal images with an average accuracy of 91% and average processing time of 3 s. The performance of the proposed algorithm in comparison to recently published algorithms and commercial imaoe analysis tool further ascertains its robustness.

New hybrid count- and geometry-based method for quantification of left ventricular volumes and ejection fraction from ECG-gated SPECT: Methodology and validation

We have previously developed a new method for quantitative assessment of left ventricular (LV) volumes and ejection fraction (EF) from electrocardiography-gated single photon emission computed tomography (SPECT). The aims of this study were to present the methodology, to validate the gated SPECT cardiac quantification (GSCQ) method in phantoms and patients, and to determine normal values of LVEF. A simple thresholding technique was used to generate binary images from nongated SPECT images. The K-means cluster classification algorithm was used to separate the LV region from non-LV regions on the binary images. A count- and geometry-based algorithm was applied to define endocardial and epicardial boundaries for calculation of LV volumes and LVEF. Overall correlation between GSCQ-quantified volumes and actual phantom volumes was good ( r = 0.97 and standard error of estimation (SEE) = 9.99 mL for normal phantoms, r = 0.99 and SEE = 6.97 mL for phantoms with defects). In patient studies, LVEF derived by GSCQ from SPECT and from equilibrium radionuclide angiography also showed good correlation ( r = 0.90 and SEE = 6.2%). The lower limit of normal LVEF from 8-frame gated SPECT by use of GSCQ was 45%. Quantification of LVEF by the GSCQ method was highly producible and was not significantly affected by the presence of myocardial perfusion defects or intense gastrointestinal activity. The GSCQ method provides reliable and consistent assessments of LV volumes and EF. This methodology is less affected by intense gastrointestinal activity than other methods.