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Remote Sensing And Image Interpretation

Student dropout is a serious problem globally. It affects not only the individual who drops out but also the former school, family, and society in general. With the current development of science and technology, big data is emphasized as the most significant technology in data analysis. From the recorded educational data, efficient prediction of students' dropout is currently a hot topic of research. Previous studies have focused only on the students' dropout based on specific levels such as individual, middle school, and university level. However, ensemble learning approaches have not received much research attention so far to predict students' dropout in university classes based on rare datasets. In this paper, we propose a novel stacking ensemble based on a hybrid of Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and Feed-forward Neural Networks (FNN) to predict student's dropout in university classes. On the dataset collected from 2016 to 2020 at Constantine the Philosopher University in Nitra, the proposed method has demonstrated greater performance when compared with the base models using testing accuracy and the area under the curve (AUC) evaluation metrics under the same conditions. Based on the findings of this study, students at the risk of dropping out the school can be identified based on influential factors and different agents of education can refer to this information for early intervention in the uncontrolled behavior that can lead to the risk of dropping out and take proactive precautionary measures before the issue arise. 

Predicting student's dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization

Aiming at the problemsof too much iterative times in selecting initial centroids stochastically for K-Means algorithm,a method is proposed to optimize the initial centroids through cutting the set into k segmentations and select one point in each segmentation as initial centroids for iterative computing. A new valid function called clustering-index is defined as the sum of clustering-density and clustering-significance and can be used to search the optimization of k in the internal of [1,n(1/2) ]. The simulation experiment with IRIS data set shows that the proposed algorithm converges faster and the value k found is close to the actual value,which proves the validity of the algorithm.

An Improved K-Means Clustering Algorithm

Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches. Its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward, if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, irrespective of the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies, that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data, which are constructed to pose difficult clustering problems and in two real-world applications.

Swarm intelligence for self-organized clustering

Analysis of meteorological conditions in Spain by means of clustering techniques

Data clustering has found significant applications in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. There are several types of data clustering such as partitional, hierarchical, spectral, density-based, mixture-modeling to name a few. Among these, partitional clustering is well suited for most of the applications due to the less computational requirement. An analysis of various literatures available on partitional clustering will not only provide good knowledge, but will also lead to find the recent problems in partitional clustering domain. Accordingly, it is planned to do a comprehensive study with the literature of partitional data clustering techniques. In this paper, thirty three research articles have been taken for survey from the standard publishers from 2005 to 2013 under two different aspects namely the technical aspect and the application aspect. The technical aspect is further classified based on partitional clustering, constraint-based partitional clustering and evolutionary programming-based clustering techniques. Furthermore, an analysis is carried out, to find out the importance of the different approaches that can be adopted, so that any new development in partitional data clustering can be made easier to be carried out by researchers. Comprehensive Study and Analysis of Partitional Data Clustering Techniques

Comprehensive Study and Analysis of Partitional Data Clustering Techniques

Clustering is a distribution of data into groups of similar objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The concept of clustering applications is particularly in the context of information retrieval and in organizing web resources. The objective of clustering is to find out information and in the present day context, to locate most relevant resources. In data mining, 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. Though the K-Means is one of the best clustering algorithms, the quality is based on the starting condition and it may converge to local minima. There is not much of work done by the researchers to improve the cluster quality after grouping. We have proposed a novel method to improve the cluster quality on high dimensional data set by ant based refinement algorithm. The Ant Colony Optimization algorithm (ACO) is one of the most widely used probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. An ant is a simple computational agent in the ant colony optimization algorithm. It develops an iterative solution for any problem at hand. The intermediate solutions can be used to arrive at the final solution. The proposed algorithm is tested using data from different domain and the results show that refined initial starting points and post processing refinement of clusters based on ACO can lead to improved solutions in terms of entropy, time taken and accuracy of clusters.

Enhancement of K-Means algorithm using ACO as an optimization technique on high dimensional data

Data clustering is one of the major areas of research in data mining. Of late, high dimensionality dataset is becoming popular because of the generation of huge volumes of data. Among the traditional partitional clustering algorithms, the bisecting K-Means is one of the most widely used for high dimensional dataset. But the performance degrades as the dimensionality increases. Also, the task of selection of cluster for further bisection is a challenging one. To overcome these drawbacks, we incorporate two constraints namely, stability-based measure and Mean Square Error (MSE) on the novel partitional clustering method, CHB-K-Means algorithm. In the experimental analysis, the performance is analyzed with respect to computation time and clustering accuracy as the number of outliers detected varies. We infer that an average clustering accuracy of 75 % has been achieved and the computation time taken for cluster formation also decreases as more number of outliers is detected.

Effect of Outlier Detection on Clustering Accuracy and Computation Time of CHB K-Means Algorithm

Incorporating Stability and Error-based Constraints for a Novel Partitional Clustering Algorithm

The concept of Data Clustering is considered to be very significant in various application areas like text mining, fraud detection, health care, image processing, bioinformatics etc. Due to its application in a variety of domains, various techniques are presented by many research domains in the literature. Data Clustering is one of the important tasks that make up Data Mining. Clustering can be classified into different types such as partitional, hierarchical, spectral, density-based, grid-based, model based etc. Among the different types of clustering available, partitional clustering is the most widely used in most of the applications since the computation involved is not very complex. Hence lot of research has been carried out in clustering using partitional method. In this paper, it is proposed to do a comprehensive study of the different partitional clustering techniques used in the literature which will also provide an insight into the recent problems in the same area. In this paper, sixteen research articles have been taken which are published by different publishers between the years 2005 and 2013. Various algorithms come under partitional clustering among which Bisecting K-Means is an excellent one that gives a good quality output for clustering large number of data. Also a broad analysis is carried out to provide an insight into the importance of the various approaches which can in turn throw light to developments in the same area.

https://www.rroij.com/open-access/a-detailed-study-and-analysis-of-differentpartitional-data-clustering-techniques.pdf

A Detailed Study and Analysis of differentPartitional Data Clustering Techniques

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