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Int J Appl Inf Syst 4(5):30вЂ“38Д«isson G, Grimal C (2012) Co-clustering of multi-view datasets: a parallelizable approach. Shah N, Mahajan S (2012) Document clustering: a detailed review. The experimental results show that the proposed similarity functions can gauge the similitude between documents more accurately than the existing metrics, and the proposed clustering scheme surpasses considerably up-to-date algorithms. Later, we aggregated these similarity matrices to obtain a unified similarity matrix and apply spectral clustering algorithm on it to generate the final clusters. Afterward, we ran clustering algorithms on these similarity matrices to evaluate the performance of single view. Firstly, CS, ED, triangleвЂ™s area similarity and sectorвЂ™s area similarity metric, and our five similarity metrics have been applied to every view of a dataset to generate a corresponding similarity matrix. Furthermore, we proposed our multi-view document clustering plan which dependent on the proposed similarity metric.

This methodology foils the downside of the CS and ED similarity metrics by figuring the divergence between documents with the same ED while thinking about their sizes. In this paper, we originally created five models of similarity metric. The second canвЂ™t register the divergence of two vectors that offer a similar ED. The first did not consider the magnitude difference (MD) between the two vectors. Existing multi-view document clustering methods broadly utilized two measurements: the Cosine similarity (CS) and the Euclidean distance (ED). A challenging problem in document clustering is the similarity metric. Numerous works implemented multi-view clustering algorithms in document clustering.

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