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The achievement of good honours in Undergraduate degrees is important in the context of Higher Education (HE), both for students and for the institutions that host them. In this paper, we look at whether data mining can be used to highlight performance problems early on and propose remedial actions. Furthermore, some of the methods may also form the basis for recommender systems that may guide students...
Data perturbation aims to disguise original data values so the confidential information is kept safe and the disclosure risk is minimized. In this article, we exploit the characteristics of nonmetric multidimensional scaling (NMDS) to transform data and generate perturbed data. We hypothesize that NMDS is a good tool for privacy‐preserving data clustering. For this, our model should preserve the distance...
Selecting a “good” clustering solution is one of the major difficulties in clustering data as there are many possible clustering solutions for a given problem, including solutions that contain varying numbers of clusters. Our objective is to select measures of clustering quality that can be applied in a multi-objective optimisation context. Such measures may represent potentially conflicting objectives...
Many techniques have been proposed to protect the privacy of data outsourced for analysis by external parties. However, most of these techniques distort the underlying data properties, and therefore, hinder data mining algorithms from discovering patterns. The aim of Privacy-Preserving Data Mining (PPDM) is to generate a data-friendly transformation that maintains both the privacy and the utility...
The most successful multi-objective metaheuristics, such as NSGA II and SPEA 2, usually apply a form of elitism in the search. However, there are multi-objective problems where this approach leads to a major loss of population diversity early in the search. In earlier work, the authors applied a multi-objective metaheuristic to the problem of rule induction for predictive classification, minimizing...
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