Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data lay on a non linear manifol...
The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of on...
Wei Li 0010, Xuerui Wang, Ruofei Zhang, Ying Cui, ...
In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a f...
One essential issue of document clustering is to estimate the appropriate number of clusters for a document collection to which documents should be partitioned. In this paper, we ...
The problem of graph classification has attracted great interest in the last decade. Current research on graph classification assumes the existence of large amounts of labeled tra...
Contextual processing is a new emerging field based on the notion that information surrounding an event lends new meaning to the interpretation of the event. Data mining is the pr...
Gregory Vert, Anitha Chennamaneni, S. Sitharama Iy...
This paper describes our experience with applying data mining techniques to the problem of fraud detection in spatio-temporal health data in Medicare Australia. A modular framework...
Kee Siong Ng, Yin Shan, D. Wayne Murray, Alison Su...
Abstract--This paper presents a framework for privacypreserving Gaussian Mixture Model computations. Specifically, we consider a scenario where a central service wants to learn the...
Targeting advertising on television is difficult due to limitations around ad tracking and ad delivery. This paper describes a new method of television advertising which can work w...