Minits-AllOcc: An Efficient Algorithm for Mining Timed Sequential Patterns

2021 ◽  
pp. 668-685
Author(s):  
Somayah Karsoum ◽  
Clark Barrus ◽  
Le Gruenwald ◽  
Eleazar Leal
Keyword(s):  
Efficient Algorithm ◽  
Sequential Patterns

An efficient algorithm for Hiding High Utility Sequential Patterns

2018 ◽  
Vol 95 ◽  
pp. 77-92 ◽  
Author(s):  
Bac Le ◽  
Duy-Tai Dinh ◽  
Van-Nam Huynh ◽  
Quang-Minh Nguyen ◽  
Philippe Fournier-Viger
Keyword(s):  
Efficient Algorithm ◽  
Sequential Patterns ◽  
High Utility

scholarly journals SPaMi-FTS: An Efficient Algorithm for Mining Frequent Sequential Patterns

2014 ◽  
pp. 470-477
Author(s):  
José Kadir Febrer-Hernández ◽  
José Hernández-Palancar ◽  
Raudel Hernández-León ◽  
Claudia Feregrino-Uribe
Keyword(s):  
Efficient Algorithm ◽  
Sequential Patterns

Mining Top-K Click Stream Sequences Patterns

2016 ◽  
Vol 4 (3) ◽  
pp. 655
Author(s):  
MEHDI Haj Ali ◽  
Qun-Xiong Zhu ◽  
Yan-Lin He
Keyword(s):  
Data Mining ◽  
Efficient Algorithm ◽  
Pattern Mining ◽  
Search Space ◽  
Extensive Study ◽  
Sequential Patterns ◽  
Support Threshold ◽  
Search Space Pruning ◽  
High Support ◽  
Pruning Methods

<p><em>Sequential pattern mining, it  is not just important in data mining field , but  it is the basis of many applications .However, running applications cost time and memory, especially when dealing with dense of the dataset. Setting the proper minimum support threshold is one of the factors that consume more memory and time. However ,  it is difficult for users to get the appropriate patterns, it may present too many sequential patterns  and makes it difficult for users to comprehend the results. The problem becomes worse and worse when dealing with long click stream sequences or huge dataset. As a solution, we developed an efficient algorithm, called TopK (Top-K click stream sequence pattern mining), which employs the output as top-k patterns , K is the most important and relevant frequencies (with a high support) . However ,our algorithm based on pseudo-projection to avoid consuming more time and memory, and uses several efficient search space pruning methods together with BI-Directional Extension. Our extensive study and experiments on real click stream datasets show TopK significantly outperforms the previous algorithms.</em></p>

e-NSPFI: Efficient Mining Negative Sequential Pattern from Both Frequent and Infrequent Positive Sequential Patterns

2017 ◽  
Vol 31 (02) ◽  
pp. 1750002 ◽  
Author(s):  
Yongshun Gong ◽  
Tiantian Xu ◽  
Xiangjun Dong ◽  
Guohua Lv
Keyword(s):  
Association Rules ◽  
Efficient Algorithm ◽  
Optimization Method ◽  
Negative Association ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Negative Association Rules ◽  
Storage Optimization

Negative sequential patterns (NSPs), which focus on nonoccurring but interesting behaviors (e.g. missing consumption records), provide a special perspective of analyzing sequential patterns. So far, very few methods have been proposed to solve for NSP mining problem, and these methods only mine NSP from positive sequential patterns (PSPs). However, as many useful negative association rules are mined from infrequent itemsets, many meaningful NSPs can also be found from infrequent positive sequences (IPSs). The challenge of mining NSP from IPS is how to constrain which IPS could be available used during NSP process because, if without constraints, the number of IPS would be too large to be handled. So in this study, we first propose a strategy to constrain which IPS could be available and utilized for mining NSP. Then we give a storage optimization method to hold this IPS information. Finally, an efficient algorithm called Efficient mining Negative Sequential Pattern from both Frequent and Infrequent positive sequential patterns (e-NSPFI) is proposed for mining NSP. The experimental results show that e-NSPFI can efficiently find much more interesting negative patterns than e-NSP.

An efficient algorithm for mining periodic high-utility sequential patterns

2018 ◽  
Vol 48 (12) ◽  
pp. 4694-4714 ◽  
Author(s):  
Duy-Tai Dinh ◽  
Bac Le ◽  
Philippe Fournier-Viger ◽  
Van-Nam Huynh
Keyword(s):  
Efficient Algorithm ◽  
Sequential Patterns ◽  
High Utility

scholarly journals Mining negative sequential patterns from frequent and infrequent sequences based on multiple level minimum supports

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1765-1776
Author(s):  
Ping Qiu ◽  
Xiaoqi Jiang ◽  
Feng Hao ◽  
Tiantian Xu ◽  
Xiangjun Dong
Keyword(s):  
Efficient Algorithm ◽  
Intelligent Systems ◽  
Experimental Results ◽  
Sequential Patterns ◽  
Multiple Level ◽  
Time Efficiency ◽  
Minimum Support

Negative sequential patterns (NSP) are critical and sometimes much more informative than positive sequential patterns (PSP) in many intelligent systems and applications. However, the existing NSP algorithms do not allow negative items being contained in an element except the NegI-NSP algorithm, which can obtain many meaningful sequences with negative items in an element. NegI-NSP, however, hasn?t considered the following problems: (1) it uses a single minimum support to all size sequences, which is unfair to a long size sequence; (2) it only mines NSP from PSP, not from infrequent positive sequences (IPS), which also contain many useful NSP. So we propose an efficient algorithm, named MLMS-NSP, to mine NSP based on multiple level minimum supports (MLMS) from PSP and IPS. Firstly, MLMS scheme is proposed by assigning different minimum supports to sequences with different sizes. Secondly, IPS are constrained by combining MLMS, and then the NSP is obtained from these IPS. Finally, experimental results show that the MLMS-NSP algorithm can effectively mine NSP from IPS, and the time efficiency is higher than using single minimum support.

An Efficient Algorithm for Sequential Pattern Mining with Time Constraints

2011 ◽  
Vol 341-342 ◽  
pp. 530-534
Author(s):  
Zai Ping Tao
Keyword(s):  
Efficient Algorithm ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Time Constraints ◽  
Time Index ◽  
Index Sets ◽  
Efficient Processing

In this paper, a new algorithm named TCSP is proposed to mine sequential patterns with different time constraints. It scans the database into memory and constructs time-index sets for efficient processing. It mines the desired sequential patterns without generating any candidates. We have evaluated the new algorithm with the well-known GSP algorithm and the DELISP algorithm for various datasets and constraints. The comprehensive experiments show that the TCSP algorithm works better and it has good scalability.

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