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In this paper, we propose a practical approach for extracting the most relevant paragraphs from the original document to form a summary for Thai text. The idea of our approach is to exploit both the local and global properties of paragraphs. The local property can be considered as clusters of significant words within each paragraph, while the global property can be though of as relations of all paragraphs in a document. These two properties are combined for ranking and extracting summaries. Experimental results on real-world data sets are encouraging.

Nowadays, clustering is a popular tool for exploratory data analysis, such as K-means and Fuzzy C-mean. Automatic determination of the initialization number of clusters in K-means clustering application is often needed in advance as an input parameter to the algorithm. In this paper, a method has been developed to determine the initialization number of clusters in satellite image clustering application using a data mining algorithm based on the co-occurrence matrix technique. The proposed method was tested using data from unknown number of clusters with multispectral satellite image in Thailand. The results from the tests confirm the effectiveness of the proposed method in finding the initialization number of clusters and compared with isodata algorithm.

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