An Efficient Weighted Negative Sequence Pattern Mining Algorithm with Multiple Minimum Support

机译：具有多个最小支持的有效加权负序模式挖掘算法

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The traditional negative sequence pattern mining algorithm is based on the same importance and probability of occurrence of each item, but it is different in the actual database. Based on the Neg-GSP algorithm, this paper proposes a weighted negative sequence pattern mining algorithm based on multiple minimum support. In the negative sequence pattern mining, different weights are set for the item, and multiple minimum support is introduced to mine the weighted frequently negative sequence patterns. The experiment uses the UCI data set to prove the accuracy and effectiveness of the algorithm, and has some improvements in the negative sequence patterns mining.

机译：传统的负序列模式挖掘算法基于每个项目的相同重要性和概率，但在实际数据库中是不同的。基于NEG-GSP算法，本文提出了一种基于多个最小支持的加权负序模式挖掘算法。在负序列模式挖掘中，为该项目设置不同的权重，并引入多个最小支持以挖掘加权频繁的负序列模式。实验使用UCI数据集来证明算法的准确性和有效性，并且对负序列模式采集的一些改进。

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《International Conference on Applications and Techniques in Cyber Intelligence》|2020年|xxxii 1091 pages :|共10页
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Dongyuan Wang; He Jiang; Aixin Yang;
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中图分类 TP18-532;
关键词
Data mining; Multiple minimum support; Weighted; Negative sequence pattern;

机译：数据挖掘;多个最小支持;加权;负序列模式;

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1. An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports [J] . Ya-Han Hu, Fan Wu, Yi-Jiun Liao The Journal of Systems and Software . 2013,第5期

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2. A New Frequent Pattern Mining Algorithm with Weighted Multiple Minimum Supports [J] . Zhang Haoran, Zhang Jianwu, Wei Xuyang, Intelligent automation and soft computing . 2017,第4期

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3. Frequent Itemset Mining Using LP-Growth Algorithm Based on Multiple Minimum Support Threshold Value (Multiple Item Support Frequent Pattern Growth) [J] . M. Sinthuja, N. Puviarasan, P. Aruna Journal of computational and theoretical nanoscience . 2019,第4期

机译：使用基于多个最小支持阈值的LP-生长算法（多项支持频繁模式增长）频繁的项目集挖掘
4. An Efficient Weighted Negative Sequence Pattern Mining Algorithm with Multiple Minimum Support [C] . Dongyuan Wang, He Jiang, Aixin Yang International Conference on Applications and Techniques in Cyber Intelligence . 2020

机译：具有多个最小支持的有效加权负序模式挖掘算法
5. Efficient algorithms for identification and analysis of repetitive patterns in biological sequences [D] . Zheng, Jie 2006

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6. Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism [O] . Ya-Han Hu, Yen-Liang Chen -1

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7. HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS [O] . Truong Chi Tin, Tran Ngoc Anh, Duong Van Hai, 2019

机译：HupSMT：具有多个最低实用阈值的不确定数据库中的高实用概率序列的高效算法
8. Efficient algorithms and data structures in support of DNA mapping and sequence analysis. Progress report, February 1991--February 1992 [R] . Gusfield, D, Lawler, EL, Balasubramanian, K, 1992

机译：高效的算法和数据结构，支持DNa映射和序列分析。进展报告，1991年2月至1992年2月

An Efficient Weighted Negative Sequence Pattern Mining Algorithm with Multiple Minimum Support

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