e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns

Xiang, Dong; Gong, Yongshun; Cao, Longbing

doi:10.1109/tcyb.2018.2869907

Cited by 52 publications

(19 citation statements)

References 45 publications

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“…Negative sequential pattern (NSP) mining referring to frequent sequences with non-occurring items plays an important role in many real-life applications, such as health and medical systems and risk management [20]. Although a few algorithms [21][22][23] have been proposed to mine NSP based on a minimum support threshold, they do not consider utility. This means some important sequences with low frequencies will be lost.…”

Section: High Utility Negative Sequential Pattern Mining (Hunsp Mining)mentioning

confidence: 99%

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Zhang

et al. 2020

Symmetry

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As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.

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Section: High Utility Negative Sequential Pattern Mining (Hunsp Mining)mentioning

confidence: 99%

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

Zhang

et al. 2020

Symmetry

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“…f-NSP uses bitset structure to effectively improve the time efficiency of e-NSP algorithm [22]. e-RNSP mine repetitive negative sequence patterns [23] and HUNSPM can mine high utility NSP [12].…”

Section: Negative Sequential Patterns Miningmentioning

confidence: 99%

“…For a k-itemset X, if sup(X) ≥ ms(k), then X is a FIS; and if ms(k) > sup(X) ≥ ms, then X is an inFIS. The positive and negative association rules can be mined from those FIS and inFIS discovered by MLMS model [23]. A 2-Level Supports model is proposed in [25] to discover FIS and inFIS.…”

Section: Useful Information Mining From Infrequent Patternsmentioning

confidence: 99%

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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“…This effectively improves the efficiency of e-NSP. f-NSP use bitset structure to effectively improve the time efficiency of e-NSP algorithm [28] and e-RNSP mine repetitive negative sequence patterns [29]. SAPNSP [30], SAP [31], SAPSD [6] and SAPBN [6] first mine NSP by e-NSP algorithm, and then select actionable NSP by different methods.…”

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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e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns

Cited by 52 publications

References 45 publications

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

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

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