New Exact Concise Representation of Rare Correlated Patterns: Application to Intrusion Detection

Bouasker, Souad; Hamrouni, Tarek; Yahia, Sadok Ben

doi:10.1007/978-3-642-30220-6_6

Cited by 10 publications

(9 citation statements)

References 14 publications

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“…These two sets are defined [3] as follows: While integrating the frequency constraint with the correlation constraint, we can distinguish between two sets of correlated patterns, which are the "Frequent correlated patterns" set and the "Rare correlated patterns" set. Now, based on these two previous sets, the concise exact representation of frequent correlated patterns was studied and proposed in [3], in addition to the concise exact representation of rare correlated patterns which was proposed in [6]. …”

Section: Concise Exact Representations Of Correlated Patternsmentioning

confidence: 99%

“…In [3] and in [6], the authors introduced concise exact representations of respectively frequent correlated and rare correlated patterns. The proposed approaches are based on the concept of correlated equivalence classes induced by the f bond closure operator associated to the bond measure.…”

Section: Concise Exact Representations Of Correlated Patternsmentioning

confidence: 99%

“…Datasets and Experimental Environment: Experiments were carried out on different dense and sparse benchmark datasets (6) . All the tests were carried out on a PC equipped with a 2.40 GHz Intel Core TM i3 processor and 2.92 GB of main memory, running the Linux Ubuntu 10.04.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…As an illustration of the rare correlated patterns applications in the field of medicine, the rare combination of symptoms can provide useful insights for doctors [25]. We cite that in [6], the authors proposed a concise exact representation of the set of rare correlated patterns and designed a rule-based classification process.…”

Section: Introduction and Motivationsmentioning

confidence: 97%

See 3 more Smart Citations

Inferring Knowledge from Concise Representations of Both Frequent and Rare Jaccard Itemsets

Bouasker¹,

Yahia

2013

Lecture Notes in Computer Science

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Abstract. Correlated pattern mining has become increasingly an important task in data mining and knowledge discovery. Recently, concise exact representations dedicated for frequent correlated and for rare correlated patterns according to the Jaccard measure were presented. In this paper, we offer a new method of inferring new knowledge from the introduced concise representations. A new generic approach, called GMJP, allowing the extraction of the sets of frequent correlated patterns, of rare correlated patterns and their associated concise representations is introduced. Pieces of new knowledge in the form of associations rules can be either exact or approximate. We also illustrate the efficiency of our approach over several data sets and we prove that Jaccard-based classification rules have very encouraging results.

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Section: Concise Exact Representations Of Correlated Patternsmentioning

confidence: 99%

Section: Concise Exact Representations Of Correlated Patternsmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introduction and Motivationsmentioning

confidence: 97%

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Inferring Knowledge from Concise Representations of Both Frequent and Rare Jaccard Itemsets

Bouasker¹,

Yahia

2013

Lecture Notes in Computer Science

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“…In practice, the exploitation of correlated patterns is hampered by the high number of the generated patterns. Thus, the integration of the constraint of frequency with the constraint of correlation has been proved to be very interesting by mining Frequent correlated patterns [2,14] and Rare correlated patterns [4,3]. In this situation, the main task concerns the manipulation of the constraints of correlation and of frequency.…”

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confidence: 98%

Key correlation mining by simultaneous monotone and anti-monotone constraints checking

Bouasker

Yahia

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Correlated pattern mining has become increasingly an important task in data mining and knowledge discovery. In practice, the exploitation of correlated patterns is hampered by the high number of the generated patterns. Thus, the integration of the constraint of frequency with the constraint of correlation has been proved to be very interesting by mining Frequent correlated patterns [2,14] and Rare correlated patterns [4,3]. In this situation, the main task concerns the manipulation of the constraints of correlation and of frequency. One way to deal with this issue is to mine all the correlated patterns and then to filter by the constraint of frequency. However, this filtering is done as a post-processing phase and it suffers from the important number of patterns and loses the opportunity to exploit the selectivity power of both constraints.In this paper, we introduce an approach that puts the focus on mining rare correlated patterns according to the bond measure. We were based on the simultaneous integration of the anti-monotone constraint of correlation and the monotone constraint of rarity during the mining process. Our experimental studies shows an important benefit when early pushing the constraints of distinct types. We also flag out better performances than the Gmjp approach [3] which also dealt with both types of constraints.

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Mining Interesting Disjunctive Association Rules from Unfrequent Items

Hilali¹,

Jen²,

Laurent³

et al. 2014

Communications in Computer and Information Science

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New Exact Concise Representation of Rare Correlated Patterns: Application to Intrusion Detection

Cited by 10 publications

References 14 publications

Inferring Knowledge from Concise Representations of Both Frequent and Rare Jaccard Itemsets

Inferring Knowledge from Concise Representations of Both Frequent and Rare Jaccard Itemsets

Key correlation mining by simultaneous monotone and anti-monotone constraints checking

Mining Interesting Disjunctive Association Rules from Unfrequent Items

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