A Constraint-based Mining Approach for Multi-attribute Index Selection

Given a sample of binary random vectors with i.i.d. 1 − p), we first establish a formula for the mean of the size of the random Galois lattice built from this sample, and a more

In fact, they browse the search space by traversing the lattice of frequent itemsets from an equivalence class to another, and they calculate the closure of the frequent

Thus, storage method has advantages if the number of max- imal frequent itemsets is small, but for the instances with a lot of maximal frequent itemsets, which take long time to

However, it is important to avoid building the complete trie, but only some upper part of it, so that the nodes (i.e. their root paths) represent reasonable candidates for

In the rest of this section, we discuss our experimental results with respect to three main axes: borders distribution of frequent itemsets, stability of borders distribution

In this paper, we proposes the ClosedPattern global constraint to capture the closed frequent pattern mining problem without requiring reified constraints or extra variables..

This paper is organized as follows: Section 2 informally introduces our model language, Section 3 formally defines our language for specifying protocols, Section 4 presents an

The paper estab- lishes a surprisingly simple characterization of Palm distributions for such a process: The reduced n-point Palm distribution is for any n ≥ 1 itself a log Gaussian