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IJHPCN
2008
94views more  IJHPCN 2008»
15 years 7 months ago
Analysing and improving clustering based sampling for microprocessor simulation
: We propose a set of statistical metrics for making a comprehensive, fair, and insightful evaluation of features, clustering algorithms, and distance measures in representative sa...
Yue Luo, Ajay Joshi, Aashish Phansalkar, Lizy Kuri...
ICDE
2010
IEEE
222views Database» more  ICDE 2010»
15 years 6 months ago
Finding Clusters in subspaces of very large, multi-dimensional datasets
Abstract— We propose the Multi-resolution Correlation Cluster detection (MrCC), a novel, scalable method to detect correlation clusters able to analyze dimensional data in the ra...
Robson Leonardo Ferreira Cordeiro, Agma J. M. Trai...
KDD
2009
ACM
206views Data Mining» more  KDD 2009»
16 years 8 months ago
Ranking-based clustering of heterogeneous information networks with star network schema
A heterogeneous information network is an information network composed of multiple types of objects. Clustering on such a network may lead to better understanding of both hidden s...
Yizhou Sun, Yintao Yu, Jiawei Han
166
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PKDD
2004
Springer
277views Data Mining» more  PKDD 2004»
16 years 29 days ago
Scalable Density-Based Distributed Clustering
Clustering has become an increasingly important task in analysing huge amounts of data. Traditional applications require that all data has to be located at the site where it is scr...
Eshref Januzaj, Hans-Peter Kriegel, Martin Pfeifle
AI
2005
Springer
16 years 1 months ago
A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets
Spatial clustering is an active research area in spatial data mining with various methods reported. In this paper, we compare two density-based methods, DBSCAN and DBRS. First, we ...
Xin Wang, Howard J. Hamilton