This paper proposes a two-step graph partitioning method to discover constrained clusters with an objective function that follows the well-known minmax clustering principle. Compar...
The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-super...
Computational diagnosis of cancer is a classification problem, and it has two special requirements on a learning algorithm: perfect accuracy and small number of features used in t...
Abstract. Given a point query Q in multi-dimensional space, K-Nearest Neighbor (KNN) queries return the K closest answers in the database with respect to Q. In this scenario, it is...
This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the ...
Abstract. Traditional clustering algorithms are based on one representation space, usually a vector space. However, in a variety of modern applications, multiple representations ex...
Karin Kailing, Hans-Peter Kriegel, Alexey Pryakhin...
Inductive queries are queries to an inductive database that generate a set of patterns in a data mining context. Inductive querying poses new challenges to database and data mining...
In many real world applications, systematic analysis of rare events, such as credit card frauds and adverse drug reactions, is very important. Their low occurrence rate in large da...
Jie Chen, Hongxing He, Graham J. Williams, Huidong...
Abstract. Tree structures are used extensively in domains such as computational biology, pattern recognition, XML databases, computer networks, and so on. One important problem in ...