Abstract. One of the more challenging problems faced by the data mining community is that of imbalanced datasets. In imbalanced datasets one class (sometimes severely) outnumbers t...
Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining. In this paper, a novel ...
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have be...
Recent studies have proposed different methods for mining frequent episodes. In this work, we study the problem of mining closed episodes based on minimal occurrences. We study the...
Abstract. Finding correlation clusters in the arbitrary subspaces of highdimensional data is an important and a challenging research problem. The current state-of-the-art correlati...
Data mining applications are typically used in the decision making process. The Knowledge Discovery Process (KDD process for short) is a typical iterative process, in which not on...
We propose a method to train a cascade of classifiers by simultaneously optimizing all its stages. The approach relies on the idea of optimizing soft cascades. In particular, inst...
The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particular...
Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao,...
Linked or networked data are ubiquitous in many applications. Examples include web data or hypertext documents connected via hyperlinks, social networks or user profiles connected...
Jing Gao, Feng Liang, Wei Fan, Chi Wang, Yizhou Su...