Prediction of students’ employability using clustering algorithm: A hybrid approach

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

Due to the growing interest in data mining and the educational system, educational data mining is the emerging topic for research community. The various techniques of data mining like classification and clustering can be applied to bring out hidden knowledge from the educational data. Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches for web video mining using traditional image processing (signal processing) and metadata based approach. In this paper, we focus on the education data mining and precisely MOOCs which constitute a new modality of e-learning and clustering techniques. We present a methodology that can be used for mining Moocs videos using metadata as leading contribution for knowledge discovery.

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

This paper illustrates how to use data mining techniques to help in advising students and predicting their academic performance. Data mining is used to get previously unknown, hidden and perhaps vital knowledge from a large amount of data. It combines domain knowledge, advanced analytical skills, and a vast knowledge base to reveal hidden patterns and trends that are applicable in virtually any sector ranging from engineering to medicine, to business. However, it is possible for educational institutes to use data mining to find useful information from their databases. This is usually called Educational Data Mining (EDM). Advancing the field of EDM with new data analysis techniques and new machine learning algorithms is vital. Classification and clustering techniques will be used in this project to study and analyse student performance. The key importance of this project is that it discusses different data mining techniques in the literature review to study student behaviour depending upon their performance. We tried to identify the most suitable algorithms from the existing research methods to predict the success of students. Various data mining approaches were discussed and their results were evaluated. In this paper, the J48 algorithm was applied to the data set, gathered from Umm Al-Qura University in Makkah.

Clustering algorithm is a crucial step before to analysis object's feature in image applications. The adapt DB-PSO patterns clustering algorithms (ADPCA) combined the particle swarm optimization (PSO) clustering algorithm and adapt DB_index measuring methodology to efficiently decide the real number of clusters, cluster centers, and then to recognize the correct catalog even if there are exiting some cases in various shapes, multi-dimension, real life training patterns and image datasets. In general, the PSO is adapted for dealing complex and global optimization problems. The population-based evolutional PSO learning algorithm with the self-adapt mathematic index can fit the data vibration to perform the real criterion of homogeneity of neighboring pixels in many image vision and understanding cases. Owing to the purpose of generating automatic clustering algorithms, the specific fitness function contains the DB_validity measure to significantly improve resolutions of spatial information among the given training patterns. The computation of image DB_index is delivered to retrieve the specific objects by evaluating the characters of given patterns. The novel ADPCA actually indicate the homogeneity region of interesting pictures and eliminate small pieces of elements by the supports of DB index measure, which can be used to dynamically compute the maximal similarity and small difference of the discussed image patterns. Several artificial datasets include the three-dimensional dataset with five spherical clusters, two-dimensional patterns with three different sizes circles, one Chtree Fractal image patterns, one real life IRIS data and one grey level image data, which are given as training patterns to demonstrate the adaptation and efficiency of the ADPCA learning method. It presents that ADPCA determine the correct clustering number and suitable cluster position in different data clustering examples. Two image segmentation applications also show that ADPCA can achieve correct detection of subjects. In conclusion, several simulations compared with the traditional k-means algorithm demonstrate the great results of ADPCA learning machine.

In this paper, a novel chaos particle swarm optimization(PSO) clustering algorithm for texture image segmentation was proposed. By means of stochastic property and ergodicity of chaos search mechanism, chaos PSO algorithm can effectively avoid getting into the local convergence. The proposed method combined chaos PSO algorithm with nearest neighbor clustering theorem, which can enhance the efficiency and precision of clustering. And the proposed chaos PSO clustering algorithm was applied to texture image segmentation with gray level co-occurrence matrix. The experimental results indicate that the proposed method can get better segmentation results than PSO algorithm and effectively improve the segmentation precision of texture image.Keywordsparticle swarm optimizationchaostexture segmentationgray level co-occurrence matrix (GLCM)

This paper explores the ability of a swarm of small-scale robots to optimize robot locomotion speed if only extremely limited on-board sensing capability is granted for each individual robot. This is done to assess benefits of swarm optimization versus individual control. The optimization of simulated robot locomotion speed is evaluated under multiple classical and machine learning algorithms. Furthermore, a new algorithm, Clustered Particle Swarm Optimization (CPSO), is developed based on existing optimization and clustering algorithms. The algorithms are based on an experimentally-validated nominal dynamic model for small-scale robots, with environmental parameters extended over a wider range of uncertainty. The nonlinear relationship between multiple actuation parameters and robot performance, specifically robot speed in this work, encourages a numerical method to search for a set of actuation parameters that leads to a higher robot speed. Three algorithms are presented and compared, including Simulated Annealing (SA), Particle Swarm Optimization (PSO), and CPSO. The comparison between SA and PSO demonstrates the utility of optimizing robots in a swarm. By increasing the number of robots, the optimizing iteration number can be reduced. The comparison of simulation of PSO and CPSO shows a 23% improvement with minimal sensing assumed from the robots. The single sensing feature, selected manually using phase plots, is the average foot height at a certain actuation phase in each cycle.

To improve the performance of the adhesive layer defect detection (DD) planar array capacitor system, an adhesive layer DD method based on particle swarm optimization and fuzzy C-means (FCM) clustering algorithm is proposed. By classifying erroneous capacitance data, the experiment distinguishes the capacitance values at defect locations from those at defect-free locations. In response to the presence of artifacts in the image reconstruction results, the optimal threshold method is used to post-process the reconstructed images using the mixed regularization method. These experiments confirmed that FCM clustering reduced the mean absolute deviation (MAD) of data by about 6.1%, and the proposed clustering algorithm reduced the MAD of data by about 23.2%. Compared to the Tikhonov method, the mixed regularization method reduced the relative error of reconstructed images by about 13.1% and increased the correlation coefficient of reconstructed images by about 21.6%. Therefore, this research method had significant effects in reducing data fluctuations and errors, effectively improving the image reconstructing quality and the accuracy of DD. This study can effectively improve the accuracy of adhesive layer DD, help reduce production costs, and improve production efficiency.

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization

Text clustering is a task of grouping similar documents into a cluster while assigning the dissimilar ones in other clusters.A well-known clustering method which is the K-means algorithm is extensively employed in many disciplines.However, there is a big challenge to determine the number of clusters using K-means. This paper presents a new clustering algorithm, termed Gravity Firefly Clustering (GF-CLUST) that utilizes Firefly Algorithm for dynamic document clustering. The GF-CLUST features the ability of identifying the appropriate number of clusters for a given text collection, which is a challenging problem in document clustering. It determines documents having strong force as centers and creates clusters based on cosine similarity measurement.This is followed by selecting potential clusters and merging small clusters to them. Experiments on various document datasets, such as 20 Newgroups, Reuters-21578 and TREC collection are conducted to evaluate the performance of the proposed GF-CLUST. The results of purity, F-measure and Entropy of GF-CLUST outperform the ones produced by existing clustering techniques, such as K-means, Particle Swarm Optimization (PSO) and Practical General Stochastic Clustering Method (pGSCM).Furthermore, the number of obtained clusters in GF-CLUST is near to the actual number of clusters as compared to pGSCM.

In recent years, many studies have emerged about regarding the topic of school failure, showing a growing interest in determining the multiple factors that may influence it [1]. Most of the researches that attempt to solve this issue [2] are focused on determining the factors that most affect the performance of students (dropout and failure) at the different educational levels (basic, middle and higher education) through the use of the large amount of information that current computer equipment allows to store in databases. All these data constitute a real gold mine of valuable information about students. But, identifying and finding useful and hidden information in large databases is a difficult task [3]. A very promising solution to achieve this goal is the use of knowledge mining techniques or data mining in education, which has resulted in so-called Educational Data Mining (EDM) [4]. This new area of research is concerned with the development of methods for exploring data in education, as well as the use of these methods to better understand students and the contexts where they learn [5].

Data mining is a technique for extraction of valuable patterns from multiple sources. Data mining plays an important role in marketing, electronic-commerce, business intelligent, healthcare and social network analysis. Advancement in these applications, many researchers show their interest in development of data mining applications in educational context. Educational data mining is a technique defined as a scientific area making inventions within rear types of data that derived from educational surroundings. This Paper reviews different case studies based on data mining educational systems. These systems and mining methods are considered for gathering and analysis of information. Due to huge amount of data in Educational databases, it becomes very challenging to evaluate student performance. Currently in Pakistan, there is dire need to monitor and examine student’s academic progress. There are two main causes of why existing systems were not able to analyze performance of students. First, the study on present evaluation methods is still not satisfactory to analyze the appropriate methods for evaluating the progress and performance of students in institutions of Pakistan. Second is because of absence of investigations on parameters; that effects student’s success in specific courses. Thus, a comprehensive review is proposed on evaluation of student’s performance by using techniques of Data Mining methods to progress student’s achievements. The aim of paper is to improve students’ academic performance by identifying most suitable attributes by using techniques of EDM.

The Outlier detection is very active area of research in data mining where outlier is a mismatched data in dataset with respect to the other available data.In existing approaches the outlier detection done only on numeric dataset.For outlier detection if we use clustering method , then they mainly focus on those elements as outliers which are lying outside the clusters but it may possible that some of the unknown elements with any possible reasons became the part of the cluster so we have to concentrate on that also.The Proposed method uses hybrid approach to reduce the number of outliers.The number of outlier can only reduce by improving the cluster formulation method.The proposed method uses two data mining techniques for cluster formulation i.e. weighted k-means and neural network where weighted kmeans is the clustering technique that can apply on text and date data set as well as numeric data set.Weighted kmeans assign the weights to each element in dataset.The output of weighted k-means becomes the input for neural network where the neural network is the classification and clustering technique of data mining.Training is provided to the neural network and according to that neurons performed the testing.The neural network test the cluster formulated by weighted k-means to ensure that the clusters formulated by weighted k-means are group accordingly.There is lots of outlier detection methods present in data mining.The proposed method use Integrating Semantic Knowledge (SOF) for outlier detection.This method detects the semantic outlier where the semantic outlier is a data point that behaves differently with other data points in the same class or cluster.The main motive of this research work is to reduce the number of outliers by improving the cluster formulation methods so that outlier rate reduces and also to decrease the mean square error and improve the accuracy.The simulation result clearly shows that proposed method works pretty well as it significantly reduces the outlier.

Throughout endless experimental work, short records of dynamic molecular data are generated from time to time. Biomechanics data mining and knowledge discovery have become an important study area to turn the abundance of generated raw data into pieces of information. In data mining, researchers often encounter challenging issues and constraints, ranging from nature of the collected microarray data and developed clustering algorithms to informative discovery for rhythmic data decision-making processes. This article presents the review of the commonly practiced clustering techniques in molecular biomechanical systems towards better applications in bioengineering research. It highlights the constraints and challenges encountered in temporal molecular bioengineering mechanisms. The findings revealed that the molecular data are commonly analyzed based on data mining computation and mathematical applications to link both developmental stages interfaces and the mechanical principles of living organisms. In this area, mathematical analyses are extensively carried out to investigate dynamic microarray using clustering techniques. The main goal is to extract informative knowledge. Therefore, in order to derive collective patterns and reliable information from microarray, there is a need to consider effects from the nature of data, clustering algorithms and knowledge discovery processes which require substantial understanding on biological systems. Keywords: Clustering, data mining, gene expression, information, knowledge discovery, molecular biomechanics, molecular data, bioengineering mechanisms, mathematical applications, microarray data, Clustering Algorithms.