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.

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.

Mining Repetitive Sequential Patterns without Overlapping from Sequence Database

2014 ◽  
Vol 644-650 ◽  
pp. 2097-2100
Author(s):  
Xi Qing Han ◽  
Yong Shun Gong ◽  
Xiang Jun Dong ◽  
Rui Lian Hou
Keyword(s):  
Repetitive Sequence ◽  
Experimental Results ◽  
Sequential Patterns ◽  
Sequence Mining ◽  
Sequence Database ◽  
Data Sequence ◽  
Minimum Support ◽  
Support Threshold ◽  
The Times ◽  
Set Up

Taking repetitive property into consideration can help the analyst to capture more useful information. However, most of the existing algorithms of repetitive sequence mining are used for DNA or genome, and there are very few researches to mine such patterns from sequence database. So in this paper, we (1) propose a method to clearly determine the times that a sequence appears in a data sequence; (2) propose a method to ensure the support range of repetitive sequence still within [0,100%] so as to let users set up minimum support threshold in a traditional way; and (3) propose an algorithm, RptGSP, to efficiently mine such repetitive patterns in sequence database by improving the classic algorithm GSP. Experimental results show that RptGSP is very efficient.

scholarly journals DRL-Prefixspan: A novel pattern growth algorithm for discovering downturn, revision and launch (DRL) sequential patterns

2012 ◽  
Vol 2 (4) ◽  
Author(s):  
Aloysius George ◽  
D. Binu
Keyword(s):  
Data Mining ◽  
Efficient Algorithm ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Product Launch ◽  
Sequential Patterns ◽  
Sequential Mining ◽  
One Step

AbstractDiscovering sequential patterns is a rather well-studied area in data mining and has been found many diverse applications, such as basket analysis, telecommunications, etc. In this article, we propose an efficient algorithm that incorporates constraints and promotion-based marketing scenarios for the mining of valuable sequential patterns. Incorporating specific constraints into the sequential mining process has enabled the discovery of more user-centered patterns. We move one step ahead and integrate three significant marketing scenarios for mining promotion-oriented sequential patterns. The promotion-based market scenarios considered in the proposed research are 1) product Downturn, 2) product Revision and 3) product Launch (DRL). Each of these scenarios is characterized by distinct item and adjacency constraints. We have developed a novel DRL-PrefixSpan algorithm (tailored form of the PrefixSpan) for mining all length DRL patterns. The proposed algorithm has been validated on synthetic sequential databases. The experimental results demonstrate the effectiveness of incorporating the promotion-based marketing scenarios in the sequential pattern mining process.

FINITELY SUBSEQUENTIAL TRANSDUCERS

2003 ◽  
Vol 14 (06) ◽  
pp. 983-994 ◽  
Author(s):  
CYRIL ALLAUZEN ◽  
MEHRYAR MOHRI
Keyword(s):  
Speech Recognition ◽  
Finite Number ◽  
Efficient Algorithm ◽  
Experimental Results ◽  
Theoretical Formulation ◽  
Large Vocabulary ◽  
Finite State Transducers ◽  
Finite State ◽  
Large Vocabulary Speech Recognition

Finitely subsequential transducers are efficient finite-state transducers with a finite number of final outputs and are used in a variety of applications. Not all transducers admit equivalent finitely subsequential transducers however. We briefly describe an existing generalized determinization algorithm for finitely subsequential transducers and give the first characterization of finitely subsequentiable transducers, transducers that admit equivalent finitely subsequential transducers. Our characterization shows the existence of an efficient algorithm for testing finite subsequentiability. We have fully implemented the generalized determinization algorithm and the algorithm for testing finite subsequentiability. We report experimental results showing that these algorithms are practical in large-vocabulary speech recognition applications. The theoretical formulation of our results is the equivalence of the following three properties for finite-state transducers: determinizability in the sense of the generalized algorithm, finite subsequentiability, and the twins property.

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

scholarly journals Adaptive Double-Exploration Tradeoff for Outlier Detection

2020 ◽  
Vol 34 (04) ◽  
pp. 6837-6844
Author(s):  
Xiaojin Zhang ◽  
Honglei Zhuang ◽  
Shengyu Zhang ◽  
Yuan Zhou
Keyword(s):  
Confidence Interval ◽  
Outlier Detection ◽  
Real World ◽  
Efficient Algorithm ◽  
Experimental Results ◽  
Sample Complexity ◽  
Bandit Problem ◽  
Real World Datasets ◽  
Synthetic Datasets ◽  
The Individual

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold. Distinct from the traditional TBP, the threshold is defined as a function of the rewards of all the arms, which is motivated by the criterion for identifying outliers. The learner needs to explore the rewards of the arms as well as the threshold. We refer to this problem as "double exploration for outlier detection". We construct an adaptively updated confidence interval for the threshold, based on the estimated value of the threshold in the previous rounds. Furthermore, by automatically trading off exploring the individual arms and exploring the outlier threshold, we provide an efficient algorithm in terms of the sample complexity. Experimental results on both synthetic datasets and real-world datasets demonstrate the efficiency of our algorithm.

scholarly journals Simple primitive recognition via hierarchical face clustering

2020 ◽  
Vol 6 (4) ◽  
pp. 431-443
Author(s):  
Xiaolong Yang ◽  
Xiaohong Jia
Keyword(s):  
Hierarchical Clustering ◽  
Test Data ◽  
Efficient Algorithm ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Experimental Results ◽  
Face Clustering ◽  
Wide Range ◽  
Hierarchical Clustering Algorithm ◽  
Bottom To Top

AbstractWe present a simple yet efficient algorithm for recognizing simple quadric primitives (plane, sphere, cylinder, cone) from triangular meshes. Our approach is an improved version of a previous hierarchical clustering algorithm, which performs pairwise clustering of triangle patches from bottom to top. The key contributions of our approach include a strategy for priority and fidelity consideration of the detected primitives, and a scheme for boundary smoothness between adjacent clusters. Experimental results demonstrate that the proposed method produces qualitatively and quantitatively better results than representative state-of-the-art methods on a wide range of test data.

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