Clustering is an exploratory data analysis task. It aims to find the intrinsic structure of data by organizing data objects into similarity groups or clusters. It is often called unsupervised learning because no class labels denoting an a priori partition of the objects are given. This is in contrast with supervised learning (e.g., classification) for which the data objects are already labeled ...

Decision trees, either classification or regression trees, are especially attractive type of models for three main reasons. First, they have an intuitive representation, the resulting model is easy to understand and assimilate by humans [BFOS84]. Second, the decision trees are nonparametric models, no intervention being required from the user, and thus they are very suited for exploratory knowl...

Correctness verification of query results is a significant challenge in database outsourcing. Most of the proposed approaches impose high overhead, which makes them impractical in real scenarios. Probabilistic approaches are proposed in order to reduce the computation overhead pertaining to the verification process. In this paper, we use the notion of trust as the basis of our probabilistic app...

Motivated by the challenges encountered in sawmill production planning, we study a multi-product, multi-period production planning problem with uncertainty in the quality of raw materials and consequently in processes yields, as well as uncertainty in products demands. As demand and yield own different uncertain natures, they are modeled separately and then integrated. Demand uncertainty is con...

Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FPTree construction, besides the prior knowledge on support threshold,...

Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on a scenario model of future environment developments. A good approximation of the underlying stochastic process may involve a very large number of scenarios and their probabilities. We discuss the case when enough data paths can be generated, but due to solva...

Lexicalized Tree Adjoining Grammars (LTAGs) [6] are tree rewriting systems consisting of a finite set of trees associated with lexical items, so-called elementary trees (etrees). Elementary trees represent extended projections of lexical items that encapsulate all their syntactic/semantic arguments. Due to this property, semantics construction in LTAG can be based on elementary trees as basic u...