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» T-Time: Threshold-Based Data Mining on Time Series
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GFKL
2006
Springer
108views Data Mining» more  GFKL 2006»
13 years 11 months ago
Identifying and Exploiting Ultrametricity
We begin with pervasive ultrametricity due to high dimensionality and/or spatial sparsity. How extent or degree of ultrametricity can be quantified leads us to the discussion of va...
Fionn Murtagh
VLDB
2007
ACM
197views Database» more  VLDB 2007»
14 years 7 months ago
Indexable PLA for Efficient Similarity Search
Similarity-based search over time-series databases has been a hot research topic for a long history, which is widely used in many applications, including multimedia retrieval, dat...
Qiuxia Chen, Lei Chen 0002, Xiang Lian, Yunhao Liu...
WWW
2007
ACM
14 years 8 months ago
Why we search: visualizing and predicting user behavior
The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper...
Eytan Adar, Daniel S. Weld, Brian N. Bershad, Stev...
GRC
2010
IEEE
13 years 8 months ago
Local Pattern Mining from Sequences Using Rough Set Theory
Abstract--Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns ...
Ken Kaneiwa, Yasuo Kudo
SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
13 years 9 months ago
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Levent Ertöz, Michael Steinbach, Vipin Kumar