Fuzzy Frequent Pattern Mining in Spike Trains

Muiño, David Picado; León, Iván Castro; Borgelt, Christian

doi:10.1007/978-3-642-34156-4_27

Cited by 11 publications

(10 citation statements)

References 19 publications

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“…In the case of graded synchrony, however, we desire a synchrony operator that yields a degree of synchrony between 0 and 1. Such a synchrony operator is described in 9 , based on the notion of an influence map as it was introduced in 22 . An influence map describes the vicinity around an event in which synchrony with other events is expedient and can be defined formally as…”

Section: Frequent Item Set Miningmentioning

confidence: 99%

“…Although CoCoNAD solves the problems caused by time binning (particularly the boundary problem, cf. 22 ), it does not take the precision of synchrony in the individual pattern instances into account. A straightforward extension with a notion of graded synchrony requires to solve a maximum weight independent set problem, for which the greedy algorithm of the binary case no longer guarantees the optimal solution and which is NP-complete in the general case 14 .…”

Section: Introductionmentioning

confidence: 99%

“…A straightforward extension with a notion of graded synchrony requires to solve a maximum weight independent set problem, for which the greedy algorithm of the binary case no longer guarantees the optimal solution and which is NP-complete in the general case 14 . In order to avoid these complications, an efficient approximation scheme for the support was presented in 9 , based on an earlier conceptual approach using influence maps and their overlap 22 .…”

Section: Introductionmentioning

confidence: 99%

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Mining Frequent Synchronous Patterns based on Item Cover Similarity

Ezennaya-Gomez¹,

Borgelt²

2018

IJCIS

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In previous work we presented CoCoNAD (Continuous-time Closed Neuron Assembly Detection), a method to find significant synchronous patterns in parallel point processes with the goal to analyze parallel neural spike trains in neurobiology 3,9. A drawback of CoCoNAD and its accompanying methodology of pattern spectrum filtering (PSF) and pattern set reduction (PSR) is that it judges the (statistical) significance of a pattern only by the number of synchronous occurrences (support). However, the same number of occurrences can be significant for patterns consisting of items with a generally low occurrence rate, but explainable as a chance event for patterns consisting of items with a generally high occurrence rate, simply because more item occurrences produce more chance coincidences of items. In order to amend this drawback, we present in this paper an extension of the recently introduced CoCoNAD variant that is based on influence map overlap support (which takes both the number of synchronous events and the precision of synchrony into account), namely by transferring the idea of Jaccard item set mining to this setting: by basing pattern spectrum filtering upon item cover similarity measures, the number of coincidences is related to the item occurrence frequencies, which leads to an improved sensitivity for detecting synchronous events (or parallel episodes) in sequence data. We demonstrate the improved performance of our method by extensive experiments on artificial data sets.

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Section: Frequent Item Set Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mining Frequent Synchronous Patterns based on Item Cover Similarity

Ezennaya-Gomez¹,

Borgelt²

2018

IJCIS

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“…Though much work has been done with related data mining software, we still face the problem that the transaction database to analyze is imperfect in many applications [22] [23]. It is a challenge to perform serviceobjected frequent itemset mining in these applications.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Improvement of Eclat Algorithm Based on Support in Frequent Itemset Mining

Yu¹,

Wang²

2014

JCP

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Finding frequent itemsets is computationally the most expensive step in association rules mining, and most of the research attention has been focused on it. With the observation that support plays an important role in frequent item mining, in this paper, a conjecture on support count is proved and improvements of traditional Eclat algorithm are presented. The new Bi-Eclat algorithm sorted on support: Items sort in descending order according to the frequencies in transaction cache while itemsets use ascending order of support during support count. Compared with traditional Eclat algorithm, the results of experiments show that the Bi-Eclat algorithm gains better performance on several public databases given. Furthermore, the Bi-Eclat algorithm is applied in analyzing combination principles of prescriptions for Hepatitis B in Traditional Chinese Medicine, which shows its efficiency and effectiveness in practical usefulness.

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“…Although this approach solves most of the problems of a time binning approach (specifically, the boundary problem, cf. [12]), it still suffers from some shortcomings. The shortcoming we focus on in this paper is that a binary notion of synchrony (that is, a group of events is either synchronous, namely if the events all occur within a certain limited time span, or not, namely if they occur farther apart) may not be so well suited for some applications.…”

Section: Introductionmentioning

confidence: 99%

Mining Frequent Synchronous Patterns with a Graded Notion of Synchrony

Ezennaya-Gomez

Borgelt

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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We present methods to find (significant) frequent synchronous patterns in event sequences, using a graded notion of synchrony that captures both the number of instances of a pattern as well as the precision of synchrony of its constituting events. Since transferring earlier work (using a binary notion of synchrony) poses certain problems, we opt for an efficient approximation scheme to compute the pattern support. Furthermore, we transfer methods for filtering for significant and removing induced patterns, which require adaptations. Finally, we demonstrate the effectiveness of our approach with experiments on a large number of data sets with injected synchronous patterns.

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Fuzzy Frequent Pattern Mining in Spike Trains

Cited by 11 publications

References 19 publications

Mining Frequent Synchronous Patterns based on Item Cover Similarity

Mining Frequent Synchronous Patterns based on Item Cover Similarity

Improvement of Eclat Algorithm Based on Support in Frequent Itemset Mining

Mining Frequent Synchronous Patterns with a Graded Notion of Synchrony

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