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PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency.

, , and . KDD, page 1087-1096. ACM, (2017)

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Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries., , , and . SIGMOD Conference, page 2063-2075. ACM, (2021)MoveMine: mining moving object databases., , , , , , and . SIGMOD Conference, page 1203-1206. ACM, (2010)One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning., , , , , , and . CoRR, (2023)SocialKeyboard: Proofreading Everyday Writings in Mobile Phones., , , and . CHI Extended Abstracts, page 1591-1596. ACM, (2015)Meta-Learning for Online Update of Recommender Systems., , , , , and . AAAI, page 4065-4074. AAAI Press, (2022)COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies., , , , , , and . AAAI, page 11971-11981. AAAI Press, (2022)Quality-Based Automatic Classification for Presentation Slides., , , , and . ECIR, volume 8416 of Lecture Notes in Computer Science, page 638-643. Springer, (2014)MoveMine: Mining moving object data for discovery of animal movement patterns., , , , , , , and . ACM Trans. Intell. Syst. Technol., 2 (4): 37:1-37:32 (2011)An experimental analysis of limitations of MapReduce for iterative algorithms on Spark., and . Clust. Comput., 20 (4): 3593-3604 (2017)Hello There! Is Now a Good Time to Talk?: Opportune Moments for Proactive Interactions with Smart Speakers., , , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4 (3): 74:1-74:28 (2020)