Behrouz Minaei-Bidgoli

Bio: Behrouz Minaei-Bidgoli is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 27, co-authored 189 publications receiving 3187 citations. Previous affiliations of Behrouz Minaei-Bidgoli include Islamic Azad University & Michigan State University.

Predicting student performance: an application of data mining methods with an educational Web-based system

Abstract: Newly developed Web-based educational technologies offer researchers unique opportunities to study how students learn and what approaches to learning lead to success. Web-based systems routinely collect vast quantities of data on user patterns, and data mining methods can be applied to these databases. This paper presents an approach to classifying students in order to predict their final grade based on features extracted from logged data in an education Web-based system. We design, implement, and evaluate a series of pattern classifiers and compare their performance on an online course dataset. A combination of multiple classifiers leads to a significant improvement in classification performance. Furthermore, by learning an appropriate weighting of the features used via a genetic algorithm (GA), we further improve prediction accuracy. The GA is demonstrated to successfully improve the accuracy of combined classifier performance, about 10 to 12% when comparing to non-GA classifier. This method may be of considerable usefulness in identifying students at risk early, especially in very large classes, and allow the instructor to provide appropriate advising in a timely manner.

Using genetic algorithms for data mining optimization in an educational web-based system

Abstract: This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; feature weighting is works better than just feature selection.

Adaptive clustering ensembles

Abstract: Clustering ensembles combine multiple partitions of the given data into a single clustering solution of better quality. Inspired by the success of supervised boosting algorithms, we devise an adaptive scheme for integration of multiple non-independent clusterings. Individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given data set. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are drawn to increasingly focus on the problematic regions of the input feature space. A measure of a data point's clustering consistency is defined to guide this adaptation. An empirical study compares the performance of adaptive and regular clustering ensembles using different consensus functions on a number of data sets. Experimental results demonstrate improved accuracy for some clustering structures.

Ensembles of partitions via data resampling

Abstract: The combination of multiple clusterings is a difficult problem in the practice of distributed data mining. Both the cluster generation mechanism and the partition integration process influence the quality of the combinations. We propose a data resampling approach for building cluster ensembles that are both robust and stable. In particular, we investigate the effectiveness of a bootstrapping technique in conjunction with several combination algorithms. The empirical study shows that a meaningful consensus partition for an entire set of objects emerges from multiple clusterings of bootstrap samples, given optimal combination algorithm parameters. Experimental results for ensembles with varying numbers of partitions and clusters are reported for simulated and real data sets. Experimental results show improved stability and accuracy for consensus partitions obtained via a bootstrapping technique.

Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence

Abstract: Multi objective processing can be leveraged for mining the association rules. This paper discusses the application of multi objective genetic algorithm to association rule mining. We focus our attention especially on association rule mining. This paper proposes a method based on genetic algorithm without taking the minimum support and confidence into account. In order to improve algorithm efficiency, we apply the FP-tree algorithm. Our method extracts the best rules that have best correlation between support and confidence. The operators of our method are flexible for changing the fitness. Unlike the Apriori-based algorithm, it does not depend on support. Experimental study shows that our technique outperforms the traditional methods.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Educational Data Mining: A Review of the State of the Art

Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Educational data mining: A survey from 1995 to 2005

Abstract: Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.