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KDD
2008
ACM
137views Data Mining» more  KDD 2008»
16 years 7 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto
SISAP
2008
IEEE
147views Data Mining» more  SISAP 2008»
16 years 27 days ago
An Empirical Evaluation of a Distributed Clustering-Based Index for Metric Space Databases
Similarity search has been proved suitable for searching in very large collections of unstructured data objects. We are interested in efficient parallel query processing under si...
Veronica Gil Costa, Mauricio Marín, Nora Re...
KDD
2009
ACM
208views Data Mining» more  KDD 2009»
16 years 7 months ago
A principled and flexible framework for finding alternative clusterings
The aim of data mining is to find novel and actionable insights in data. However, most algorithms typically just find a single (possibly non-novel/actionable) interpretation of th...
Zijie Qi, Ian Davidson
SDM
2009
SIAM
160views Data Mining» more  SDM 2009»
16 years 3 months ago
Discovering Substantial Distinctions among Incremental Bi-Clusters.
A fundamental task of data analysis is comprehending what distinguishes clusters found within the data. We present the problem of mining distinguishing sets which seeks to find s...
Faris Alqadah, Raj Bhatnagar
ICDE
2007
IEEE
131views Database» more  ICDE 2007»
16 years 25 days ago
Incremental Clustering of Mobile Objects
Moving objects are becoming increasingly attractive to the data mining community due to continuous advances in technologies like GPS, mobile computers, and wireless communication ...
Sigal Elnekave, Mark Last, Oded Maimon