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K-means plays an important role in different fields of data mining. However, k-means often becomes sensitive due to its random seeds selecting. Motivated by this, this article proposes an optimized k-means clustering method, named k*means, along with three optimization principles. First, we propose a hierarchical optimization principle initialized by k* seeds (k à .k) to reduce the risk of random seeds selecting, and then use the proposed ''top-n nearest clusters merging'' to merge the nearest clusters in each round until the number of clusters reaches at k. Second, we propose an ''optimized update principle'' that leverages moved points updating incrementally instead of recalculating mean and SSE of cluster in k-means iteration to minimize computation cost. Third, we propose a strategy named ''cluster pruning strategy'' to improve efficiency of k-means. This strategy omits the farther clusters to shrink the adjustable space in each iteration. Experiments performed on real UCI and synthetic datasets verify the efficiency and effectiveness of our proposed algorithm.

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Pretubulysin is a bio-precursor of highly toxic tetrapeptide tubulysins. Although pretubulysin has a much simpler chemical structure, it has similar anti-mitotic potency. A series of 2-amino-thiazole-4-carboxamides were designed and synthesized based on the structure of cemadotin. These are all novel compounds and their structures are characterized by 1 H-NMR, 13 C-NMR, and high resolution (HR)MS. The antitumor activities of these compounds were screened using the methyl thiazolyl tetrazolium colorimetric (MTT) cell viability method in MCF7 (breast cancer) and NCI-H1650 (lung cancer) cells. All the synthesized compounds 6a-n showed moderate anti-proliferation activities. Compounds 6m exhibited antitumor activity with the IC 50 value of 0.47 and 1.1 µM in MCF7 and NCI-H1650 cells, respectively.