Abstract
This paper proposes a recommending system of the similar blogs gathered with similarities between blogs according to the similarity, dividing words, for each frequency, that individual blogs have. It improved the algorithm of k-means, using the model of the habits of white ants for better performance of clustering, and showed better performance of clustering as a result of evaluating and comparing with the existing algorithm of k-means as the improved algorithm. The recommending system of similar blog was designed and embodied, using the improved algorithm. TCA can reduce clustering time and the number of moving time for clustering compare with K-means algorithm.
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