A novel algorithm for extracting frequent gradual patterns

Djamegni, Clémentin Tayou; Cabrel, Tabueu Fotso Laurent; Belise, Kenmogne Edith

doi:10.1016/j.mlwa.2021.100068

Cited by 3 publications

(28 citation statements)

References 7 publications

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“…Observing the degree of memory saturation during gradual pattern discovery using Grite, SGrite, & Graank methods involved preprocessing to reduce the dataset size [5] in the case of very correlate or dense data. This adaptation is necessary to carry out the extraction.…”

Section: Specific Objective 2: Guarantee Extraction In Large Databasesmentioning

confidence: 99%

“…Definition 1. Gradual item [1,[5][6][7][8]: A gradual item is of the form A * , where A is an attribute and * ∈ {≤, ≥, <, > } and expresses a variation on the values of attribute A. If the comparison operator * is equal to ≥ (resp.…”

Section: Definitionsmentioning

confidence: 99%

“…They are interpreted as "the more age increases", "the more age decreases" & "the more salary increases". [5][6][7][8][9][10]: A gradual itemset, denoted {(A i , * i), i = 1 . .…”

Section: Definitionsmentioning

confidence: 99%

“…Definition 3. Complementary gradual pattern [1,5,9]: if a gradual pattern M = {(A * i i ), i = 1 . .…”

Section: Definition 2 Gradual Itemsetmentioning

confidence: 99%

“…From Definitions 3 & 4 we can deduce the two properties allowing a significant pruning of the search space. The first property is the equality property of gradual support [1,5,11] & the second is the anti-monotonicity property of gradual support [1,5,10,11]. Definition 5.…”

Section: Notementioning

confidence: 99%

See 4 more Smart Citations

An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite)

Fotso¹,

Cabrel²

2021

BIJCS

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Since automatic knowledge extraction must be performed in large databases, empirical studies are already showing an explosion in the search space for generalized patterns & even more so for frequent gradual patterns. In addition to this we also observe a generation of a very large number of relevant extracted patterns. Faced with this problem, many approaches have been developed, with the aim of reducing the size of the search space & by the waiting time for detection, for end users, of relevant patterns. the objective is to make decisions or refine their analyzes within a reasonable & realistic time frame. The gradual pattern mining algorithms common in large databases are CPU intensive. It is a question for us of proposing a new approach which allows an extraction of the maximum frequent gradual patterns based on a technique of partitioning data sets. MSPGrite, a new, more efficient hybrid algorithm, is the result of the new method. Experiments on numerous sets of well-known datasets support the suggested method.

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Section: Specific Objective 2: Guarantee Extraction In Large Databasesmentioning

confidence: 99%

Section: Definitionsmentioning

confidence: 99%

“…They are interpreted as "the more age increases", "the more age decreases" & "the more salary increases". [5][6][7][8][9][10]: A gradual itemset, denoted {(A i , * i), i = 1 . .…”

Section: Definitionsmentioning

confidence: 99%

“…Definition 3. Complementary gradual pattern [1,5,9]: if a gradual pattern M = {(A * i i ), i = 1 . .…”

Section: Definition 2 Gradual Itemsetmentioning

confidence: 99%

Section: Notementioning

confidence: 99%

See 3 more Smart Citations

An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite)

Fotso¹,

Cabrel²

2021

BIJCS

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show abstract

Clustering-based gradual pattern mining

Owuor,

Runkler,

Laurent

et al. 2023

Int. J. Mach. Learn. & Cyber.

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An efficient hybrid by partitioning approach for extractingmaximal gradual patterns in large databases (MPSGrite)

Cabrel¹

2022

BIJCS

View full text Add to dashboard Cite

Since automatic knowledge extraction must be performed in large databases, empirical studies are alreadyshowing an explosion in the search space for generalized patterns and even more so for frequent gradual patterns.In addition to this, we also observe a generation of a very large number of relevant extracted patterns. Being facedwith this problem, many approaches have been developed, with the aim of reducing the size of the search spaceand the waiting time for detection, for end users, of relevant patterns. The objective is to make decisions or refinetheir analyses within a reasonable and realistic time frame. The gradual pattern mining algorithms common in largedatabases are CPU intensive. It is a question for us of proposing a new approach that allows an extraction ofthe maximum frequent gradual patterns based on a technique of partitioning datasets. The new technique leadsto a new, more efficient hybrid algorithm called MSPGrite. The experiments carried out on several sets of knowndatasets justify the proposed approach.

show abstract

A novel algorithm for extracting frequent gradual patterns

Cited by 3 publications

References 7 publications

An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite)

An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite)

Clustering-based gradual pattern mining

An efficient hybrid by partitioning approach for extractingmaximal gradual patterns in large databases (MPSGrite)

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