We present AntVis,a web-based visual analytics tool for exploring ant movement data collected from the video recording of ants moving on tree branches.Our goal is to enable domain experts to visually explore massive a...We present AntVis,a web-based visual analytics tool for exploring ant movement data collected from the video recording of ants moving on tree branches.Our goal is to enable domain experts to visually explore massive ant movement data and gain valuable insights via effective visualization,filtering,and comparison.This is achieved through a deep learning framework for automatic detection,segmentation,and labeling of ants,ant movement clustering based on their trace similarity,and the design and development of five coordinated views(the movement,similarity,timeline,statistical,and attribute views)for user interaction and exploration.We demonstrate the effectiveness of AntVis with several case studies developed in close collaboration with domain experts.Finally,we report the expert evaluation conducted by an entomologist and point out future directions of this study.展开更多
提出了一种改进的自适应蚁群聚类算法(improved adaptive ant clustering,IAAC)。该算法改进了原来的AM(ant movement)模型,并在此基础上提出了一种网格化的移动策略来改善蚂蚁移动的随机性,使蚂蚁有意识地往模式较多的区域移动,极大地...提出了一种改进的自适应蚁群聚类算法(improved adaptive ant clustering,IAAC)。该算法改进了原来的AM(ant movement)模型,并在此基础上提出了一种网格化的移动策略来改善蚂蚁移动的随机性,使蚂蚁有意识地往模式较多的区域移动,极大地减少了蚂蚁无效的移动,使蚂蚁迅速地找到合适的位置放下模式;并提出了一种自适应调整蚂蚁运动阈值的方法以简化参数的选取,使得算法可以根据当前的聚类情况不断调整阈值,以达到更好的聚类结果。结果表明,该算法具有运行效率高、参数选取简单及自适应性等优点。展开更多
Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontol...Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontology-based semantic similarity measure is used in conjunction with the traditional vector-space-model-based measure to provide more accurate assessment of the similarity between documents. On the other, the ant behavior model is modified to pursue better algorithmic performance. Especially, the ant movement rule is adjusted so as to direct a laden ant toward a dense area of the same type of items as the ant＇s carrying item, and to direct an unladen ant toward an area that contains an item dissimilar with the surrounding items within its Moore neighborhood. Using WordNet as the base ontology for assessing the semantic similarity between documents, the proposed algorithm is tested with a sample set of documents excerpted from the Reuters-21578 corpus and the experiment results partly indicate that the proposed algorithm perform better than the standard ant-based text-clustering algorithm and the k-means algorithm.展开更多
基金the US National Science Foundation through grants IIS-1456763,IIS-1455886,CNS-1629914,CCF-1617735,and DUE-1833129the US National Institutes of Health through grant R01 GM116927.T.Hu,S.Zhu,and C.Liang conducted this work as iSURE(International Summer Undergraduate Research Experience)students at the University of Notre Dame during Summer 2017.
文摘We present AntVis,a web-based visual analytics tool for exploring ant movement data collected from the video recording of ants moving on tree branches.Our goal is to enable domain experts to visually explore massive ant movement data and gain valuable insights via effective visualization,filtering,and comparison.This is achieved through a deep learning framework for automatic detection,segmentation,and labeling of ants,ant movement clustering based on their trace similarity,and the design and development of five coordinated views(the movement,similarity,timeline,statistical,and attribute views)for user interaction and exploration.We demonstrate the effectiveness of AntVis with several case studies developed in close collaboration with domain experts.Finally,we report the expert evaluation conducted by an entomologist and point out future directions of this study.
文摘提出了一种改进的自适应蚁群聚类算法(improved adaptive ant clustering,IAAC)。该算法改进了原来的AM(ant movement)模型,并在此基础上提出了一种网格化的移动策略来改善蚂蚁移动的随机性,使蚂蚁有意识地往模式较多的区域移动,极大地减少了蚂蚁无效的移动,使蚂蚁迅速地找到合适的位置放下模式;并提出了一种自适应调整蚂蚁运动阈值的方法以简化参数的选取,使得算法可以根据当前的聚类情况不断调整阈值,以达到更好的聚类结果。结果表明,该算法具有运行效率高、参数选取简单及自适应性等优点。
基金This work was supported in part by National Natural Science Foundation of China under Grants No.70301009 and No. 70431001, and by Ministry of Education, Culture, Sports, Science and Technology of Japan under the "Kanazawa Region, Ishikawa High-Tech Sensing Cluster of Knowledge-Based Cluster Creation Project"
文摘Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontology-based semantic similarity measure is used in conjunction with the traditional vector-space-model-based measure to provide more accurate assessment of the similarity between documents. On the other, the ant behavior model is modified to pursue better algorithmic performance. Especially, the ant movement rule is adjusted so as to direct a laden ant toward a dense area of the same type of items as the ant＇s carrying item, and to direct an unladen ant toward an area that contains an item dissimilar with the surrounding items within its Moore neighborhood. Using WordNet as the base ontology for assessing the semantic similarity between documents, the proposed algorithm is tested with a sample set of documents excerpted from the Reuters-21578 corpus and the experiment results partly indicate that the proposed algorithm perform better than the standard ant-based text-clustering algorithm and the k-means algorithm.
