Nittaya Kerdprasop

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Clustering is a task of grouping data based on similarity. A popular k-means algorithm groups data by firstly assigning all data points to the closest clusters, then determining the cluster means. The algorithm repeats these two steps until it has converged. We propose a variation called weighted k-means to improve the clustering scalability. To speed up(More)
A learning content management system (LCMS) is an environment to support web-based learning content development. Primary function of the system is to manage the learning process as well as to generate content customized to meet a unique requirement of each learner. Among the available supporting tools offered by several vendors, we propose to enhance the(More)
—The purposes of this research are to find a model to forecast the electricity consumption in a household and to find the most suitable forecasting period whether it should be in daily, weekly, monthly, or quarterly. The time series data in our study was individual household electric power The result of the study showed that the ARIMA model was the best(More)
Projective clustering is a clustering technique for high dimensional data with the inherent sparsity of the data points. To overcome the unreliable measure of similarity among data points in high dimensions, all data points are projected to a lower dimensional sub-space. Principal component analysis (PCA) is an efficient method to dimensionality reduction(More)
The data clustering with automatic program such as k-means has been a popular technique widely used in many general applications. Two interesting sub-activity of clustering process are studied in this paper, selection the number of clusters and analysis the result of data clustering. This research aims at studying the clustering validation to find(More)
Currently, the rapid growth of information on the Internet makes automatic text classification play an important role to help people discovering desired information on enormous resources. Text mining, feature selection and classification algorithm have effect on the classification performance directly. In this paper, the comparative study of the text(More)
  • Weerasak Chongnguluam, Kanjana Intharachatorn, Prapatsorn Sinahawattana, Nittaya Kerdprasop
—Currently multi-core processors have been available on most personal computers. To get the maximum benefit of computational power from the multi-core architecture, we need a new design on existing algorithms and software. We propose the parallelization of the rough k-means clustering algorithm. In the rough k-means clustering algorithm, each cluster has(More)
This research aims at studying the recognition accuracy and execution time that are affected by different dimensionality reduction methods applied to the biometric image data. We comparatively study the fingerprint, face images, and handwritten signature data that are pre-processed with the two statistical based dimensionality reduction methods: principal(More)