Traditional association rules mining cannot meet the demands arising from some real applications. By considering the different values of individual items as utilities, utility mini...
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the s...
Abstract. Discriminative and generative methods provide two distinct approaches to machine learning classification. One advantage of generative approaches is that they naturally mo...
This paper presents the advances in subgroup discovery and the ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explici...
In many modern application ranges high-dimensional feature vectors are used to model complex real-world objects. Often these objects reside on different local sites. In this paper,...
Hans-Peter Kriegel, Peter Kunath, Martin Pfeifle, ...
We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the w...
Because exploratory rule discovery works with data that is only a sample of the phenomena to be investigated, some resulting rules may appear interesting only by chance. Techniques...
In this paper we extend the state-of-art of the constraints that can be pushed in a frequent pattern computation. We introduce a new class of tough constraints, namely Loose Anti-m...
Abstract. Hard margin support vector machines (HM-SVMs) have a risk of getting overfitting in the presence of the noise. Soft margin SVMs deal with this
Abstract. In this paper we discuss an image mining application of Egeria detection. Egeria is a type of weed found in various lands and water regions over San Joaquin and Sacrament...