episode mining
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2021 ◽  
pp. 1-17
Author(s):  
K. Poongodi ◽  
Dhananjay Kumar

The Frequent Episode Mining (FEM) is a challenging framework to identify frequent episodes from a sequence database. In a sequence, an ordered collection of events defines an episode, and frequent episodes are only considered by the earlier studies. Also, it doesn’t support for the serial based episode rule mining. In this work, the episode rules are mined with precise and serial based rule mining considering the temporal factor, so that, the occurrence time of the consequent is specified in contrast to the traditional episode rule mining. The proposed work has a larger number of candidates and specific time constraints to generate the fixed-gap episodes, and mining such episodes from whole sequence where the time span between any two events is a constant which is utilized to improve the proposed framework’s performance. In order to improve the efficiency, an Optimal Fixed-gap Episode Occurrence (OFEO) is performed using the Natural Exponent Inertia Weight based Swallow Swarm Optimization (NEIWSSO) algorithm. The temporal constraints significantly evaluate the effectiveness of episode mining, and a noticeable advantage of the present work is to generate optimal fixed-gap episodes for better prediction. The effective use of memory consumption and performance enhancement is achieved by developing new trie-based data structure for Mining Serial Positioning Episode Rules (MSPER) using a pruning method. The position of frequent events is updated in the precise-positioning episode rule trie instead of frequent events to reduce the memory space. The benchmark datasets Retail, Kosarak, and MSNBC is used to evaluate the proposed algorithm’s efficiency. Eventually, it is found that it outperforms the existing techniques with respect to memory consumption and execution time. On an average, the proposed algorithm achieves 28 times lesser execution time and consumes 45.5% less memory space for the highest minimum support value on the Retail dataset compared to existing methods.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 107465-107478
Author(s):  
Tao You ◽  
Yamin Li ◽  
Bingkun Sun ◽  
Chenglie Du

2019 ◽  
Vol 10 (4) ◽  
pp. 1-26 ◽  
Author(s):  
Xiang Ao ◽  
Haoran Shi ◽  
Jin Wang ◽  
Luo Zuo ◽  
Hongwei Li ◽  
...  

Author(s):  
Philippe Fournier-Viger ◽  
Peng Yang ◽  
Jerry Chun-Wei Lin ◽  
Unil Yun
Keyword(s):  

2018 ◽  
Vol 87 ◽  
pp. 83-101 ◽  
Author(s):  
Maryam Amiri ◽  
Leyli Mohammad-Khanli ◽  
Raffaela Mirandola

2017 ◽  
Vol 59 ◽  
pp. 303-315 ◽  
Author(s):  
Yu-Feng Lin ◽  
Chien-Feng Huang ◽  
Vincent S. Tseng

Author(s):  
Jerry C. C. Tseng ◽  
Jia-Yuan Gu ◽  
P. F. Wang ◽  
Ching-Yu Chen ◽  
Chu-Feng Li ◽  
...  

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