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SDM
2004
SIAM
123views Data Mining» more  SDM 2004»
13 years 6 months ago
Nonlinear Manifold Learning for Data Stream
There has been a renewed interest in understanding the structure of high dimensional data set based on manifold learning. Examples include ISOMAP [25], LLE [20] and Laplacian Eige...
Martin H. C. Law, Nan Zhang 0002, Anil K. Jain
SDM
2004
SIAM
194views Data Mining» more  SDM 2004»
13 years 6 months ago
Finding Frequent Patterns in a Large Sparse Graph
Graph-based modeling has emerged as a powerful abstraction capable of capturing in a single and unified framework many of the relational, spatial, topological, and other characteri...
Michihiro Kuramochi, George Karypis
SDM
2004
SIAM
253views Data Mining» more  SDM 2004»
13 years 6 months ago
Density-Connected Subspace Clustering for High-Dimensional Data
Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be managed without the help of efficient and effective ...
Peer Kröger, Hans-Peter Kriegel, Karin Kailin...
SDM
2004
SIAM
211views Data Mining» more  SDM 2004»
13 years 6 months ago
Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects
Databases are a key technology for molecular biology which is a very data intensive discipline. Since molecular biological databases are rather heterogeneous, unification and data...
Hans-Peter Kriegel, Peer Kröger, Alexey Pryak...
SDM
2004
SIAM
165views Data Mining» more  SDM 2004»
13 years 6 months ago
Visualizing RFM Segmentation
Segmentation based on RFM (Recency, Frequency, and Monetary) has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and imp...
Ron Kohavi, Rajesh Parekh
SDM
2004
SIAM
142views Data Mining» more  SDM 2004»
13 years 6 months ago
Learning to Read Between the Lines: The Aspect Bernoulli Model
We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and ...
Ata Kabán, Ella Bingham, T. Hirsimäki
SDM
2004
SIAM
224views Data Mining» more  SDM 2004»
13 years 6 months ago
Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules
In this paper we propose a method for grouping and summarizing large sets of association rules according to the items contained in each rule. We use hierarchical clustering to par...
Alípio Jorge
SDM
2004
SIAM
229views Data Mining» more  SDM 2004»
13 years 6 months ago
R-MAT: A Recursive Model for Graph Mining
How does a `normal' computer (or social) network look like? How can we spot `abnormal' sub-networks in the Internet, or web graph? The answer to such questions is vital ...
Deepayan Chakrabarti, Yiping Zhan, Christos Falout...
SDM
2004
SIAM
141views Data Mining» more  SDM 2004»
13 years 6 months ago
Visually Mining through Cluster Hierarchies
Similarity search in database systems is becoming an increasingly important task in modern application domains such as multimedia, molecular biology, medical imaging, computer aid...
Stefan Brecheisen, Hans-Peter Kriegel, Peer Kr&oum...