Legoland Malaysia, which opened in 2012, anchors a busy entertainment district; an outpost of Marlborough College, a posh British school, has helped launch a much-vaunted educational cluster.
Our interviewees' views agreed with a review of 44 studies using the Zelen design [ 17], in which half stated that they used the design to reduce bias, and is highlighted by the less than 50%% recruitment in several other GP educational cluster randomised trials [ 5, 18].
Previous work has shown that social and behavioral risk markers (including physical activity, diet, smoking and educational attainment) cluster [ 27], but it is not known whether the clustering is related to CHD risk or whether any associations with CHD risk are independent of the individual social and behavioral risk marker components.
In this situation, the clustering task becomes an inevitable incomplete educational data clustering task.
The main difference between VQ_fk_nps and our previous incomplete educational data clustering algorithms, K_nps and S_nps, is the formulation of the clusters of the similar objects in the feature space with the Gaussian kernel function instead of that in the data space.
Many recent educational data clustering works used the existing algorithms like k-means, expectation maximization, spectral clustering, etc.
Despite such a lack of incomplete data handling for an educational data clustering task, we are aware of many existing works on incomplete data clustering in general such as [1, 2, 7, 8, 9, 23, 25, 27].
In this paper, our work follows the kernel-based approach with vector quantization and the nearest prototype strategy by proposing a robust and effective incomplete educational data clustering algorithm, named VQ_fk_nps, where we tackle data incompleteness in the data space while doing clustering in the feature space.
Hence, our work focuses on an incomplete educational data clustering approach to the aforementioned task.
Our incomplete educational data clustering algorithm, VQ_fk_nps, is defined as: An evaluation of the proposed approach from the theoretical perspectives.
In the following, we describe our incomplete educational data clustering algorithm, VQ_fk_nps, using kernel-based vector quantization proposed in [10].
