Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
In this paper we propose algorithms for generation of frequent item sets by successive construction of the nodes of a lexicographic tree of item sets. We discuss different strategies in generation and traversal of the lexicographic tree such as breadth-first search, depth-first search, or a combination of the two. These techniques provide different trade-offs in terms of the I/O, memory, and computational time requirements. We use the hierarchical structure of the lexicographic tree to successively project transactions at each node of the lexicographic tree and use matrix counting on this reduced set of transactions for finding frequent item sets. We tested our algorithm on both real and synthetic data. We provide an implementation of the tree projection method which is up to one order of magnitude faster than other recent techniques in the literature. The algorithm has a well-structured data access pattern which provides data locality and reuse of data for multiple levels of the cache. We also discuss methods for parallelization of the TreeProjection algorithm. © 2001 Academic Press.
Imran Nasim, Melanie Weber
SCML 2024
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Masami Akamine, Jitendra Ajmera
IEICE Trans Inf Syst