scholarly journals An Efficient Incremental Mining Algorithm for Discovering Sequential Pattern in Wireless Sensor Network Environments

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 29 ◽  
Author(s):  
Xin Lyu ◽  
Hongxu Ma
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern ◽  
Sensor Data ◽  
Incremental Algorithm ◽  
Wireless Sensor ◽  
Incremental Mining ◽  
The Real ◽  
Mining Algorithm ◽  
Intelligent Decision ◽  
Mining Algorithms

Wireless sensor networks (WSNs) are an important type of network for sensing the environment and collecting information. It can be deployed in almost every type of environment in the real world, providing a reliable and low-cost solution for management. Huge amounts of data are produced from WSNs all the time, and it is significant to process and analyze data effectively to support intelligent decision and management. However, the new characteristics of sensor data, such as rapid growth and frequent updates, bring new challenges to the mining algorithms, especially given the time constraints for intelligent decision-making. In this work, an efficient incremental mining algorithm for discovering sequential pattern (novel incremental algorithm, NIA) is proposed, in order to enhance the efficiency of the whole mining process. First, a reasoned proof is given to demonstrate how to update the frequent sequences incrementally, and the mining space is greatly narrowed based on the proof. Second, an improvement is made on PrefixSpan, which is a classic sequential pattern mining algorithm with a high-complexity recursive process. The improved algorithm, named PrefixSpan+, utilizes a mapping structure to extend the prefixes to sequential patterns, making the mining step more efficient. Third, a fast support number-counting algorithm is presented to choose frequent sequences from the potential frequent sequences. A reticular tree is constructed to store all the potential frequent sequences according to subordinate relations between them, and then the support degree can be efficiently calculated without scanning the original database repeatedly. NIA is compared with various kinds of mining algorithms via intensive experiments on the real monitoring datasets, benchmarking datasets and synthetic datasets from aspects including time cost, sensitivity of factors, and space cost. The results show that NIA performs better than the existed methods.

The Sequential Pattern Mining Algorithm MHSP Based on MH

2011 ◽  
Vol 63-64 ◽  
pp. 425-430
Author(s):  
Jun Wang ◽  
Ya Qiong Jiang
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Important Method ◽  
The Real ◽  
Mining Algorithm ◽  
Large Projection ◽  
Growth Approach

Pattern growth approach is an important method in sequential pattern mining. Projection database based on the method is introduced in PrefixSpan, and the PrefixSpan algorithm can solve the problem of mining sequential patterns. But relative to large projection database, the performance of PrefixSpan is affected. Inspired by the prefix-divide method and MH structure, this paper proposed a new algorithm MHSP for sequential pattern mining. Based on the real datasets, experimental results show that the performance of MHSP algorithm is more than twice as fast as PrefixSpan.

scholarly journals An Application of Improved Gap-BIDE Algorithm for Discovering Access Patterns

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Xiuming Yu ◽  
Meijing Li ◽  
Taewook Kim ◽  
Seon-phil Jeong ◽  
Keun Ho Ryu
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern ◽  
Access Pattern ◽  
Large Database ◽  
Log Data ◽  
Web Log ◽  
Mining Algorithm ◽  
User Access ◽  
Access Patterns ◽  
Mining Algorithms

Discovering access patterns from web log data is a typical sequential pattern mining application, and a lot of access pattern mining algorithms have been proposed. In this paper, we propose an improved approach of Gap-BIDE algorithm to extract user access patterns from web log data. Compared with the previous Gap-BIDE algorithm, a process of getting a large event set is proposed in the provided algorithm; the proposed approach can find out the frequent events by discarding the infrequent events which do not occur continuously in an accessing time before generating candidate patterns. In the experiment, we compare the previous access pattern mining algorithm with the proposed one, which shows that our approach is very efficient in discovering access patterns in large database.

scholarly journals Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256329
Author(s):  
Rory Bunker ◽  
Keisuke Fujii ◽  
Hiroyuki Hanada ◽  
Ichiro Takeuchi
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Rugby Union ◽  
Sequential Pattern ◽  
Frequent Patterns ◽  
Event Sequences ◽  
Mining Algorithm ◽  
Original Dataset ◽  
And Performance ◽  
Mining Algorithms

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.

Sequential Pattern Mining Algorithm Based on Interestingness

2018 ◽  
Author(s):  
Tao Li ◽  
Shuaichi Zhang ◽  
Hui Chen ◽  
Yongjun Ren ◽  
Xiang Li ◽  
...  
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mining Algorithm

An Empirical Evaluation of Sequential Pattern Mining Algorithms

2018 ◽  
pp. 615-626
Author(s):  
Marjana Prifti Skenduli ◽  
Corrado Loglisci ◽  
Michelangelo Ceci ◽  
Marenglen Biba ◽  
Donato Malerba
Keyword(s):  
Pattern Mining ◽  
Empirical Evaluation ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mining Algorithms

A Scalable Sequential Pattern Mining Algorithm

2006 ◽  
Author(s):  
Jiahong Wang ◽  
Y. Asanuma ◽  
E. Kodama ◽  
T. Takata
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mining Algorithm

A Location-Item-Time sequential pattern mining algorithm for route recommendation

2015 ◽  
Vol 73 ◽  
pp. 97-110 ◽  
Author(s):  
Chieh-Yuan Tsai ◽  
Bo-Han Lai
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mining Algorithm ◽  
Route Recommendation
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