A Novel Key Point Based MLCS Algorithm for Big Sequences Mining

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yanni Li;Bing Liu;Tihua Duan;Zhi Wang;Hui Li;Jiangtao Cui
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引用次数: 0

Abstract

Mining multiple longest common subsequences ( MLCS ) from a set of sequences of length three or more over a finite alphabet (a classical NP-hard problem) is an important task in many fields, e.g., bioinformatics, computational genomics, pattern recognition, information extraction, etc. Applications in these fields often involve generating very long sequences (length $\geqslant$ 10,000), referred to as big sequences. Despite efforts in improving the time and space complexities of MLCS mining algorithms, both existing exact and approximate algorithms face challenges in handling big sequences due to the overwhelming size of their problem-solving graph model MLCS-DAG ( D irected A cyclic G raph), leading to the issue of memory explosion or extremely high time complexity. To bridge the gap, this paper first proposes a new identification and deletion strategy for different classes of non-critical points in the mining of MLCS , which are the points that do not contribute to their MLCS s mining in the MLCS-DAG . It then proposes a new MLCS problem-solving graph model, namely $DAG_{KP}$ (a new MLCS-DAG containing only K ey P oints). A novel parallel MLCS algorithm, called KP-MLCS ( K ey P oint based MLCS ), is also presented, which can mine and compress all MLCS s of big sequences effectively and efficiently. Extensive experiments on both synthetic and real-world biological sequences show that the proposed algorithm KP-MLCS drastically outperforms the existing state-of-the-art MLCS algorithms in terms of both efficiency and effectiveness.
基于关键点的新型大序列挖掘 MLCS 算法
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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