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- W87992352 abstract "AbstractClustering is an important unsupervised machine learning (ML) method, and single-pass (SP) clustering is a fast and low-cost method used in event detection and topic tracing. To obtain a better result, SP is always needed to run multi-times in previous literatures due to it is dependent on the order of the instances appearance, which is not the global optimum. To tackle this situation, this paper proposed a hybrid clustering based on ant colony optimization (ACO) and SP, which exploits the global optimization ability of ACO and the superiority of SP, and takes the results of SP as a positive feedback implemented in ACO to improve the quality of the clustering. Experiments on two UCI datasets show that it is better than the multi-times running of the SP according to two different evaluation measures, which verifies its effectiveness and availability.KeywordsData mining (DM)swarm intelligenceant colony optimization (ACO)single-pass (SP) clusteringtopic detection and tracing (TDT)" @default.
- W87992352 created "2016-06-24" @default.
- W87992352 creator A5063900433 @default.
- W87992352 date "2012-01-01" @default.
- W87992352 modified "2023-09-26" @default.
- W87992352 title "A Hybrid Clustering Based on ACO and Single-Pass" @default.
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- W87992352 doi "https://doi.org/10.1007/978-3-642-27296-7_10" @default.
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