Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Graph Patterns Using Disjoint Paths
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Partial Orders from Strings
IEEE Transactions on Knowledge and Data Engineering
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
IEEE Transactions on Knowledge and Data Engineering
Frequent regular itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Association rule mining, in which generating frequent patterns is a key step, is an effective way of identifying inherent and unknown interrelationships between characteristics of celestial spectra data and its physicochemical properties. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. Finally, we quantitatively evaluate the CPU and I/O performance of our novel interrelation analysis method using a variety of real-world data sets. Our experimental results show that it is practical to study the laws of celestial bodies using our new interrelation analysis method to discover correlations between celestial spectra data characteristics and the physicochemical properties.