Mining high utility itemsets in massive transactional datasets

Mining High Utility Itemsets from a transaction database is to find itemsets that have utility beyond an user-specified threshold. Existing High Utility Itemsets mining algorithms suffer from many problems when being applied to massive transactional datasets. One major problem is the high memory dependency: the gigantic data structure built is assumed to fit in the computer main memory. This paper proposes a new disk-based High Utility Itemsets mining algorithm, which achieves its efficiency by applying three new ideas. First, transactional data is converted into a new database layout called Transactional Array that prevents multiple scanning of the database during the mining phase. Second, for each frequent item, a relatively small independent tree is built for summarizing co-occurrences. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed. We have tested our algorithm on several very large transactional databases and the results show that our algorithm works efficiently

Some remarks on functional dependencies in relational datamodels

The concept of minimal family is introduced. We prove that this family and family of functional dependencies (FDs ) determine each other uniquely. A characterization of this family is presented. We show that there is no polynomial time algorithm finding a minimal family from a given relation scheme. We prove that the time complexity of finding a minimal family from a given relation is exponential in the number of attributes

On the Hypergraphs and candidate keys

The combinatorial problems are interesting in the relational  datamodel. The theory of hypergraphs was a very useful tool for the solution of combinatorial problems. The transversal and the minimal transversal are important concepts in this theory. In this paper, base on hypergraph we  give some new characterizations of the set the candidate keys in the relational datamodel