Results 231 to 240 of about 1,888,648 (281)

Efficient Big Data Clustering

Proceedings of the 22nd International Database Engineering & Applications Symposium on - IDEAS 2018, 2018
The need to support advanced analytics on Big Data is driving data scientist' interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a ...
Ianni M., Mazzeo G. M., Masciari E., Zaniolo C.   +3 more
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Cluster non‐Gaussian functional data

Biometrics, 2020
AbstractGaussian distributions have been commonly assumed when clustering functional data. When the normality condition fails, biased results will follow. Additional challenges occur as the number of the clusters is often unknown a priori. This paper focuses on clustering non‐Gaussian functional data without the prior information of the number of ...
Qingzhi Zhong, Huazhen Lin, Yi Li
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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.
D. Gondek, T. Hofmann
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Advancing data clustering via projective clustering ensembles

Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, 2011
Projective Clustering Ensembles (PCE) are a very recent advance in data clustering research which combines the two powerful tools of clustering ensembles and projective clustering.Specifically, PCE enables clustering ensemble methods to handle ensembles composed by projective clustering solutions. PCE has been formalized as an optimization problem with
Gullo F, Domeniconi C, Tagarelli A
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Data Clustering

2009
Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, including well-known clustering techniques, such as partitioning clustering, hierarchical clustering, density-based clustering and grid-based clustering, and recent advances in clustering, such as subspace ...
Yanchang Zhao, Longbing Cao, Huaifeng Zhang, Chengqi Zhang   +3 more
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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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Data Clustering

2018
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data ...
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Clustering Geostatistical Functional Data

2011
In this paper, we among functional data. A first strategy aims to classify curves spatially dependent and to obtain a spatio-functional model prototype for each cluster. It is based on a Dynamic Clustering Algorithm with on an optimization problem that minimizes the spatial variability among the curves in each cluster. A second one looks simultaneously
ROMANO, Elvira, VERDE, Rosanna
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Clustering data streams

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, N. Mishra, R. Motwani, L. O'Callaghan   +3 more
openaire   +1 more source

Clustering Multiple Data Streams

2011
In 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, Lechevallier Y, VERDE, Rosanna   +2 more
openaire   +1 more source