Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Zero-suppressed BDDs for set manipulation in combinatorial problems
DAC '93 Proceedings of the 30th international Design Automation Conference
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
The Complexity of the Optimal Variable Ordering Problems of Shared Binary Decision Diagrams
ISAAC '93 Proceedings of the 4th International Symposium on Algorithms and Computation
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
SWOD '07 Proceedings of the 2007 IEEE International Workshop on Databases for Next Generation Researchers
| Hi-index | 0.00 |
Recently, an efficient method of database analysis using Zero-suppressed Binary Decision Diagrams (ZBDDs) has been proposed. BDDs are a graph-based representation of Boolean functions, now widely used in system design and verification. Here we focus on ZBDDs, a special type of BDDs, which are suitable for handling large-scale combinatorial itemsets in frequent itemset mining. In general, it is well-known that the size of ZBDDs greatly depends on variable ordering; however, in the specific cases of applying ZBDDs to data mining, the effect of variable ordering has not been studied well. In this paper, we present a theoretical study on ZBDD variable ordering for representing frequent itemsets. We show two instances of databases we composed, where the ZBDD sizes are exponentially sensitive to the variable ordering. We also show that there is a case where the ZBDD size must be exponential in any variable ordering. Our theoretical results are helpful for developing a good heuristic method of variable ordering.