Behrouz Minaei-Bidgoli

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ae 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(More)
Web-based educational technologies allow educators to study how students learn (descriptive studies) and which learning strategies are most effective (causal/predictive studies). Since web-based educational systems collect vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships(More)
Phishing attack is a kind of identity theft which tries to steal ?confidential data like on?-?line bank account information?. In a ?phishing attack scenario, attacker deceives users by a fake email ?which is called scam. In this paper we employ three different ?learning methods to detect phishing scams. Then, we use ?ensemble methods on their results to(More)
Valency lexicons are valuable resources for natural language processing. The need for new resources for languages encourages researchers to collect new datasets. One of the most important datasets is valency lexicons. In valency lexicons, information about obligatory and optional complements of words is annotated at the syntactic and semantic levels. In(More)
Many stability measures, such as Normalized Mutual Information (NMI), have been proposed to validate a set of partitionings. It is highly possible that a set of partitionings may contain one (or more) high quality cluster(s) but is still adjudged a bad cluster by a stability measure, and as a result, is completely neglected. Inspired by evaluation(More)
In this paper a new criterion for clusters validation is proposed. Many stability measures to validate a cluster have been proposed such as Normalized Mutual Information. We propose a new criterion for clusters validation. The drawback of the common approach is discussed in this paper and then a new asymmetric criterion is proposed to assess the association(More)
In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. The clusters which satisfy a threshold of this measure are selected to participate in clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. Since(More)
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(More)
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. In this paper we propose a data resampling approach for building cluster ensembles that are both robust and stable. In particular, we(More)
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(More)