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AI
2005
Springer
14 years 3 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
CORR
2008
Springer
113views Education» more  CORR 2008»
13 years 10 months ago
Document stream clustering: experimenting an incremental algorithm and AR-based tools for highlighting dynamic trends
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, i...
Alain Lelu, Martine Cadot, Pascal Cuxac
SDM
2010
SIAM
156views Data Mining» more  SDM 2010»
13 years 11 months ago
Unsupervised Discovery of Abnormal Activity Occurrences in Multi-dimensional Time Series, with Applications in Wearable Systems
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...
Alireza Vahdatpour, Majid Sarrafzadeh
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
14 years 10 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
ICDE
2006
IEEE
144views Database» more  ICDE 2006»
14 years 11 months ago
Approximately Processing Multi-granularity Aggregate Queries over Data Streams
Aggregate monitoring over data streams is attracting more and more attention in research community due to its broad potential applications. Existing methods suffer two problems, 1...
Shouke Qin, Weining Qian, Aoying Zhou