An association rule generation algorithm usually generates too many rules including a lot of uninteresting ones. Many interestingness criteria are proposed to prune those uninteresting rules. However, they work in post-pruning process and hence do not improve the rule generation ef£ciency. In this paper, we discuss properties of informative rule set and conclude that the informative rule set in...

How to provide users a positive experience during interaction with information (i.e., the “Information eXperience” (IX)) is still an open question. As a starting point, this work investigates how the emotion of interest can be influenced by modifying the complexity of the information presented to users. The appraisal theory of interest suggests a “sweet spot” where interest will be at its peak:...

Most valid rules that are learned from very large and high dimensional data sets are not interesting, but are already known to the users. The dominant model of the overall data set may well suppress the interesting local patterns. The search for interesting local patterns can be implemented by a two step learning approach which rst acquires the global models before it focuses on the rest in ord...

In this paper, several interesting integral inequalities are presented and some open problems are proposed later on.

Component trees or level set graphs have proved to be efficient tools for the extraction of stable features in many image analysis and understanding applications. In this paper, we explore the relation between level sets as feature descriptors and curvature scale-space. While most classical image feature detectors are based on linear scale-space, affine-invariant descriptors can be related to t...

We show that current experimental constraints already severely restrict what might be observable at ee colliders. We identify some cases where it may be possible to probe physics beyond what might be possible at other facilities and make some remarks about physics capability of high energy ee colliders.

We deploy a recently proposed framework for mining subjectively interesting patterns from data [3] to the problem of clustering, where patterns are clusters in the data. This framework outlines how subjective interestingness of patterns (here, clusters) can be quantified using sound information theoretic concepts. We demonstrate how it motivates a new objective function quantifying the interest...