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Proceedings 41st Annual Symposium on Foundations of Computer Science, 2002
We study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model is relevant to new classes of applications involving massive data sets, such as Web click stream ...
S. Guha +3 more
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We study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model is relevant to new classes of applications involving massive data sets, such as Web click stream ...
S. Guha +3 more
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Clustering Multiple Data Streams
2011In recent years, data streams analysis has gained a lot of attention due to the growth of applicative fields generating huge amount of temporal data. In this paper we will focus on the clustering of multiple streams. We propose a new strategy which aims at grouping similar streams and, together, at computing summaries of the incoming data.
BALZANELLA, Antonio +2 more
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Clusters in Aggregated Health Data
2008Spatial 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.
Buchin, Kevin +5 more
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On clustering data with few clusters
2023Clustering 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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Data Clustering and Various Clustering Approaches
2017It is needed to organize the data in different groups for various purposes, where clustering is useful. The chapter covers Data Clustering in the detail, which includes; introduction to data clustering with figures, data clustering process, basic classification of clustering and applications of clustering, describing hard partition clustering and fuzzy
Shashi Mehrotra, Shruti Kohli
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2009
This chapter provides a survey of some clustering methods relevant to clustering Web elements for better information access. We start with classical methods of cluster analysis that seems to be relevant in approaching the clustering of Web data. Graph clustering is also described since its methods contribute significantly to clustering Web data.
Dušan Húsek +3 more
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This chapter provides a survey of some clustering methods relevant to clustering Web elements for better information access. We start with classical methods of cluster analysis that seems to be relevant in approaching the clustering of Web data. Graph clustering is also described since its methods contribute significantly to clustering Web data.
Dušan Húsek +3 more
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Proceedings of the International Conference on Computer Applications — Database Systems, 2010
Madhuri A. Dalal, Umesh L. Kulkarni
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Madhuri A. Dalal, Umesh L. Kulkarni
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Fuzzy Clustering of Ecological Data
1991Ordination and classification have always been important stages in ecological data analysis. This paper presents a clustering technique based on fuzzy sets to obtain both ordination and classification particularly well suited for ecological analyses.
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