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- W2357936889 abstract "Clustering technology is very important in knowledge discovery,and K-Means Clustering Algorithms is the most frequently used in clustering analysis.But K-Means Algorithms must choose the amount of classes in advance,in other words,researchers need the exact number of significant classes in data spaces.In this paper,we propose a new Clustering Algorithm aiming at the matter mentioned above.The algorithm selects dynamically K marginal similar objects as original class points and refines constantly these class points based on the discrete degree inner classes or between classes.The simulation results show that this Algorithm has improved veracity and efficiency,compared to the original algorithm." @default.
- W2357936889 created "2016-06-24" @default.
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- W2357936889 date "2004-01-01" @default.
- W2357936889 modified "2023-09-23" @default.
- W2357936889 title "Analyzing Gene Sequence Data by Dynamically Iterative Clustering Algorithms" @default.
- W2357936889 hasPublicationYear "2004" @default.
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