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» Mining Very Large Databases
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KDD
2007
ACM
182views Data Mining» more  KDD 2007»
15 years 10 months ago
Cleaning disguised missing data: a heuristic approach
In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially va...
Ming Hua, Jian Pei
KDD
2009
ACM
189views Data Mining» more  KDD 2009»
15 years 4 months ago
CoCo: coding cost for parameter-free outlier detection
How can we automatically spot all outstanding observations in a data set? This question arises in a large variety of applications, e.g. in economy, biology and medicine. Existing ...
Christian Böhm, Katrin Haegler, Nikola S. M&u...
KDD
2005
ACM
107views Data Mining» more  KDD 2005»
15 years 3 months ago
Predicting the product purchase patterns of corporate customers
This paper describes TIPPPS (Time Interleaved Product Purchase Prediction System), which analyses billing data of corporate customers in a large telecommunications company in orde...
Bhavani Raskutti, Alan Herschtal
EDBT
2012
ACM
228views Database» more  EDBT 2012»
13 years 7 days ago
Finding maximal k-edge-connected subgraphs from a large graph
In this paper, we study how to find maximal k-edge-connected subgraphs from a large graph. k-edge-connected subgraphs can be used to capture closely related vertices, and findin...
Rui Zhou, Chengfei Liu, Jeffrey Xu Yu, Weifa Liang...
KDD
2008
ACM
150views Data Mining» more  KDD 2008»
15 years 10 months ago
Hypergraph spectral learning for multi-label classification
A hypergraph is a generalization of the traditional graph in which the edges are arbitrary non-empty subsets of the vertex set. It has been applied successfully to capture highord...
Liang Sun, Shuiwang Ji, Jieping Ye