Optimal clustering from noisy binary feedback | Machine Learning
link.springer.com › Machine Learning
Mar 22, 2024 · This paper investigates the design of such systems, tackling clustering problems that have to be solved using answers to binary questions.
Oct 14, 2019 · We study the problem of clustering a set of items from binary user feedback. Such a problem arises in crowdsourcing platforms solving large-scale labeling ...
Feb 5, 2024 · Abstract. We study the problem of clustering a set of items from binary user feedback. Such a problem arises in crowdsourcing platforms ...
The objective is to devise an algorithm with a minimal cluster recovery error rate. We derive problem-specific information-theoretical lower bounds on the error ...
Mar 22, 2024 · Abstract. We study the problem of clustering a set of items from binary user feedback. Such a prob- lem arises in crowdsourcing platforms ...
Mar 22, 2024 · We study the problem of clustering a set of items from binary user feedback. Such a problem arises in crowdsourcing platforms solving ...
Feb 18, 2021 · The algorithm learns to select items hard to cluster and relevant questions more often. We compare the performance of our algorithms with or ...
People also ask
How clustering can handle noisy data?
What is the optimal number of clustering?
What is the most effective clustering algorithm?
Which clustering is used when the clusters are irregular or intertwined and when noise and outliers are present in the dataset?
Jul 16, 2016 · How would I go about making a dendrogram considering this is all binary data? Should I use Hierarchical clustering or UPGMA or something else?
Mar 23, 2017 · I am trying to clusters points in a high-dimensional space (5000 features for each data point). Each feature can take 0 or 1 value.
May 29, 2020 · A clustering algorithm that automatically determines the number of clusters and doesn't require hyperparameter tuning.