Research on Typical Algorithms in Negative Sequential Pattern Mining

Gong, Yongshun; Liu, Chuanlu; Xiang, Dong

doi:10.2174/1874444301507010934

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…E-NSP algorithm is the most efficient method to mine NSP [4]. It only uses equations to calculate the NSC's supports, so as to improve the efficiency of the algorithm.…”

Section: Negative Sequential Patterns Miningmentioning

confidence: 99%

“…Negative sequential patterns (NSP) considering both occurring items and non-occurring items (also called positive and negative items) play a very important role in many intelligent systems and applications [1] [2][3] [4][5] [6]. For instance, nsp =< xy¬zF > is a negative sequential patterns, where x, y and z stand for drug codes, and F stands for disease status.…”

Section: Introductionmentioning

confidence: 99%

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Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports

Qiu¹,

Zhao²,

Chen³

et al. 2018

Filomat

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Negative sequential patterns (NSP) referring to both occurring items (positive items) and nonoccurring items (negative items) play a very important role in many real applications. Very few methods have been proposed to mine NSP and most of them only mine NSP from frequent positive sequences, not from infrequent positive sequences (IPS). In fact, many useful NSP can be mined from IPS, just like many useful negative association rules can be obtained from infrequent itemsets. e-NSPFI is a method to mine NSP from IPS, but its constraint is very strict to IPS and many useful NSP would be missed. In addition, e-NSPFI only uses a single minimum support, which implicitly assumes that all items in the database are of the similar frequencies. In order to solve the above problems and optimize NSP mining, a 2-level multiple minimum supports (2-LMMS) constraint to IPS is proposed in this paper. Firstly, we design two minimum supports constraints to mine frequent and infrequent positive sequences. Secondly, we use Select Actionable Pattern (SAP) method to select actionable NSP. Finally, we propose a corresponding algorithm msNSPFI to mine actionable NSP from IPS with 2-LMMS. Experiment results show that msNSPFI is very efficient for mining actionable NSP.

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“…E-NSP algorithm is the most efficient method to mine NSP [4]. It only uses equations to calculate the NSC's supports, so as to improve the efficiency of the algorithm.…”

Section: Negative Sequential Patterns Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports

Qiu¹,

Zhao²,

Chen³

et al. 2018

Filomat

Self Cite

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“…NegGSP [24] generates NSC by seed sets and calculates the NSC's supports by re-scanning database. This is very time consuming [25]. Furthermore, it generates NSP by comparing the supports of NSC with single minimum support.…”

Section: The Status Of Nsp Miningmentioning

confidence: 99%

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

Qiu¹,

Jiang²,

Feng³

et al. 2018

Filomat

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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.

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“…This NSP [20] has become greatly imperative and could play a unique role in undertaking various business applications like the relations among treatment services as well as illnesses. So far, only some models are there to mine NSP [19] since the mining process is very much difficult than the PSP [1,10,22,45] mining process.…”

Section: Introductionmentioning

confidence: 99%

Hybridized optimization oriented fast negative sequential patterns mining

Yadav¹

2021

Multimed Tools Appl

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Recently, negative sequential patterns (NSP) (like missing medical treatments) mining is important in data mining research since it includes negative correlations between item sets, which are overlooked by positive sequential pattern mining (PSP) (for instance, utilization of medical service). Yet, discovering the NSP is very complex than finding PSP because of the important problem complexity occurred by high computational cost, non-occurring elements, as well as huge search space in evaluating NSC, and most of the NSP based existing works are inefficient. Therefore, this paper intends to propose a fast NSP mining algorithm for the disease prediction model. This model includes Data normalization, Data separation based on labels, and Pattern recognition phases. In the midst of data separation, the maximum occurring data is optimally selected using a new algorithm that hybridizes the FireFly (FF) algorithm and Grey Wolf Optimization (GWO). This proposed Firefly induced Grey Wolf optimization (F-GWO) algorithm automatically selects the maximum occurring information as per the PSP support. The proposed model is compared over other conventional methods with varied measures. Especially, the computation cost of our model is 46.87%, 6.27%, 9.37%, 2.76%, and 66.62% better than the existing GA, ABC, PSO, FF, and GWO models respectively.

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Research on Typical Algorithms in Negative Sequential Pattern Mining

Cited by 15 publications

References 16 publications

Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports

Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports

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

Hybridized optimization oriented fast negative sequential patterns mining

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