Affiliations: [a] School of Artificial Intelligence, Hebei University of Technology, Tianjin, China | [b] Hebei Key Laboratory of Big Data Computing, Tianjin, China | [c] School of Economics and Management, Hebei University of Technology, Tianjin, China | [d] School of Software, Dalian University of Technology, Dalian, Liaoning, China | [e] Key Laboratory of Knowledge Engineering with Big Data (The Ministry of Education), Hefei University of Technology, Hefei, Anhui, China
Correspondence:
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Corresponding author: Yan Li, School of Economics and Management, Hebei University of Technology, Tianjin, China. E-mail: lywuc@163.com.
Abstract: Pattern matching can be used to calculate the support of patterns, and is a key issue in sequential pattern mining (or sequence pattern mining). Nonoverlapping pattern matching means that two occurrences cannot use the same character in the sequence at the same position. Approximate pattern matching allows for some data noise, and is more general than exact pattern matching. At present, nonoverlapping approximate pattern matching is based on Hamming distance, which cannot be used to measure the local approximation between the subsequence and pattern, resulting in large deviations in matching results. To tackle this issue, we present a Nonoverlapping Delta and gamma approximate Pattern matching (NDP) scheme that employs the (δ,γ)-distance to give an approximate pattern matching, where the local and the global distances do not exceed δ and γ, respectively. We first transform the NDP problem into a local approximate Nettree and then construct an efficient algorithm, called the local approximate Nettree for NDP (NetNDP). We propose a new approach called the Minimal Root Distance which allows us to determine whether or not a node has root paths that satisfy the global constraint and to prune invalid nodes and parent-child relationships. NetNDP finds the rightmost absolute leaf of the max root, searches for the rightmost occurrence from the rightmost absolute leaf, and deletes this occurrence. We iterate the above steps until there are no new occurrences. Numerous experiments are used to verify the performance of the proposed algorithm.
