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ICPR
2008
IEEE
14 years 8 months ago
K-means clustering of proportional data using L1 distance
We present a new L1-distance-based k-means clustering algorithm to address the challenge of clustering high-dimensional proportional vectors. The new algorithm explicitly incorpor...
Bonnie K. Ray, Hisashi Kashima, Jianying Hu, Monin...
ISNN
2011
Springer
12 years 10 months ago
Orthogonal Feature Learning for Time Series Clustering
This paper presents a new method that uses orthogonalized features for time series clustering and classification. To cluster or classify time series data, either original data or...
Xiaozhe Wang, Leo Lopes
COMSIS
2010
13 years 5 months ago
MFI-tree: An effective multi-feature index structure for weighted query application
Multi-Feature Index Tree (MFI-Tree), a new indexing structure, is proposed to index multiple high-dimensional features of video data for video retrieval through example. MFI-Tree e...
Yunfeng He, Junqing Yu
SIGIR
2008
ACM
13 years 7 months ago
Towards breaking the quality curse.: a web-querying approach to web people search
Searching for people on the Web is one of the most common query types to the web search engines today. However, when a person name is queried, the returned webpages often contain ...
Dmitri V. Kalashnikov, Rabia Nuray-Turan, Sharad M...
CIKM
2008
Springer
13 years 9 months ago
Mining term association patterns from search logs for effective query reformulation
Search engine logs are an emerging new type of data that offers interesting opportunities for data mining. Existing work on mining such data has mostly attempted to discover knowl...
Xuanhui Wang, ChengXiang Zhai