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Co-clustering is a promising technique for summarizing cooccurrence information such as purchase history transactions and document-keyword frequencies. A close connection between fuzzy c-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms, which are induced by the
standardize research keywords. Secondly, frequent item sets with different support degrees are extracted from research proposals based on research ontology. Thirdly, a new measure of similarity degree between two research proposals is developed and then a clustering algorithm is proposed to classify research proposals based on
review. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the proposals cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore
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