Results 231 to 240 of about 803,970 (265)

Inference for Clustered Data

The Stata Journal: Promoting communications on statistics and Stata, 2018
In this article, we introduce clusteff, a community-contributed command for checking the severity of cluster heterogeneity in cluster–robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2017, Review of Economics and Statistics 99: 698–709) develop the ...
Lee, Chang Hyung, Steigerwald, Douglas G.   +1 more
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Non-redundant data clustering

Knowledge and Information Systems, 2005
Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice, this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data.
David Gondek, Thomas Hofmann 0001
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Inference on Distributed Data Clustering

Engineering Applications of Artificial Intelligence, 2005
In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present a measure of inference risk as a function of reconstruction precision and number of colluders in a distributed data mining group.
Josenildo Costa da Silva, Matthias Klusch   +1 more
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On Discrete Data Clustering

2008
Finite mixture modeling have been applied for different data mining tasks. The majority of the work done concerning finite mixture models has focused on mixtures for continuous data. However, many applications involve and generate discrete data for which discrete mixtures are better suited.
Nizar Bouguila, Walid ElGuebaly
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Clusters in Aggregated Health Data

2008
Spatial information plays an important role in the identification of sources of outbreaks for many different health-related conditions. In the public health domain, as in many other domains, the available data is often aggregated into geographical regions, such as zip codes or municipalities.
Kevin Buchin, Maike Buchin, Marc J. van Kreveld, Maarten Löffler, Jun Luo 0008, Rodrigo I. Silveira   +5 more
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Overlapping Clustering for Textual Data

Proceedings of the 2018 7th International Conference on Software and Computer Applications, 2018
Texts have inherent overlapping, therefore for clustering textual data, the overlapping clustering algorithms are more appropriate. In this regard, a major challenge is that they are very slow in clustering big volumes of textual data. Among others, OKM and OSOM are two important overlapping clustering algorithms. In this study, we have implemented and
Atefeh Khazaei, Mohammad Ghasemzadeh 0001, Dieter Gollmann   +2 more
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RDF Data Clustering

2013
The Web is evolving from a Web of Documents to a Web of Data. Meanwhile, the development of Semantic Web applications opens the way for addressing complex information needs. In this scenario, clustering is identified as a crucial task for semantic mashups.
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Multiscale Clustering for Functional Data

Journal of Classification, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yaeji Lim, Hee-Seok Oh, Ying-Kuen K. Cheung   +2 more
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On clustering data with few clusters

2023
Clustering is a problem with wide applications and is studied in various disciplines, such as unsupervised machine learning, data mining and combinatorial optimization. We study clustering in the setting with a limited number of clusters. In particular, we study the following two clustering problems:• An Algorithm for Categorical datasets: The k-modes ...
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Accelerated Sequential Data Clustering

Journal of Classification
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Reza Mortazavi, Elham Enayati, Abdolali Basiri   +2 more
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