We propose a novel MLCS mining tool I-MLCS (Integrated Multiple Longest Common Subsequence algorithm) for mining big sequence data. The algorithm includes a series of novel MLCS mining strategies, and can integrate both exact and approximate MLCS algorithms to effectively meet those challenges. Extensive experiments on both synthetic and real-world biological sequence datasets demonstrate I-MLCS’s outstanding performance in terms of both running time efficiency and result quality compared to the state-of-the-art existing exact and approximate MLCS algorithms.
In summary, the main contributions of this algorithm are:
- It reveals and verifies some serious weaknesses of current popular dominant-based point MLCS algorithms through both theoretical analysis and experiments for the first time.
- It proposes a novel problem-solving graph model, called Non-redundant Common Subsequence Graph (NCSG), and introduces a series of novel MLCS mining techniques for NCSG to harness the big sequence data efficiently and effectively.
- Based on several shared functions, it designs an novel integrated MLCS mining tool I-MLCS that integrates both exact and approximate algorithms with the ability to customize the mining precision and visualizing the mined results.
- Install JDK-1.8;
- Put the data files to be processed into the 'file' folder of this project;
- Run the following command in a shell command window.
java -jar -Dmlcs.max-thread=2 -Djava.util.Arrays.useLegacyMergeSort=true -Dmlcs.p=1 I-MLCS.jar [fileName]
- [fileName]: program parameter, files should be put in 'file' Folder to be read;
- [-Dmlcs.max-thread]: VM parameter, the number of threads you need to start;
- [-Djava.util.Arrays.useLegacyMergeSort]: VM parameter, should be set true;
- [-Dmlcs.p]: VM parameter, the range of this value is between 0 and 1, the bigger the number, the higher the accuracy;