Clustering for Petri nets

Keller, W.O.

doi:10.1016/s0304-3975(02)00597-2

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…In classical clustering each instance belongings only and only to one cluster and it cannot be a member of two or more clusters, and thus classical clustering will face with trouble in determining which instance belongs to each cluster in a state of similarity of one or two or more instances to a cluster. The main difference between classical clustering and fuzzy clustering is that in fuzzy clustering an instance can be owned by more than one cluster, namely according to fuzzy logics, clustering belonging function not two values (0 or 1) but, may have any values between 0 and 1 [12]. Fuzzy c-means algorithm: one of the most widely used clustering algorithms is c-means algorithm.…”

Section: B Classical and Fuzzy Clusteringmentioning

confidence: 99%

A New Hybrid Classification Method for Condensing of Large Datasets: A Case Study in the Field of Intrusion Detection

Khazaee¹,

Bozorgmehr²

2015

IJMECS

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In large data sets data pre-processing always has been the most essential data processing stages. Sampling and using small volumes of data has been an integrated part of data pre-processing to decrease training errors and increase speed of learning. In this study, instead of sampling from all data and using small parts of them, a method has been proposed to not only benefit from sampling but all data be used during training process. In this way, outliers would be detected and even used in completely different way. Using artificial neural networks, new features for instances will be built and the problem of intrusion detection will be mapped as a 10feature problem. In fact, such a classification is for feature creation and as features in new problem only have discrete values, in final classification decision tree will be used. The results of proposed method on KDDCUP'99 datasets and Cambridge datasets show that this has improved classification in many classes dramatically.

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Section: B Classical and Fuzzy Clusteringmentioning

confidence: 99%

A New Hybrid Classification Method for Condensing of Large Datasets: A Case Study in the Field of Intrusion Detection

Khazaee¹,

Bozorgmehr²

2015

IJMECS

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“…Hierarchical algorithm finds nested clusters either in agglomerative or in divisive [19] , and partitional algorithm divides the data sets into some clusters whose members have nothing in common with each other [16,24,43] . The most popular and extensively used algorithm among partitioning algorithms is the k -means (KM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

Farajzadeh

2015

Journal of Intelligent Systems

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AbstractAmong the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time.

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“…In general however, the two unfoldings differ. The "universality" of the construction which associates a faithful unfolding to a general Petri net was already studied by Keller in a categorical setting [8], under various finiteness conditions (on the support of the initial marking and on the pre-and postsets of every transition).…”

Section: Introductionmentioning

confidence: 99%

Branching Processes of General Petri Nets

Couvreur

Poitrenaud

Weil

2013

Fundamenta Informaticae

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We propose a new model of branching processes, suitable for describing the behavior of general Petri nets, without any finiteness or safeness assumption. In this framework, we define a new class of branching processes and unfoldings of a net N , which we call faithful. These coincide with the safe branching processes and unfoldings if N is safe, or weakly safe as in [Engelfriet 1991], but not in general. However, faithful branching processes and processes satisfy the good order-theoretic properties which make the safe processes of safe nets so useful in practice, and which are known not to hold for the safe processes of a general net. Faithful processes represent therefore good candidates to generalize the theory of safe nets to the general case.

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Clustering for Petri nets

Cited by 3 publications

References 24 publications

A New Hybrid Classification Method for Condensing of Large Datasets: A Case Study in the Field of Intrusion Detection

A New Hybrid Classification Method for Condensing of Large Datasets: A Case Study in the Field of Intrusion Detection

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Branching Processes of General Petri Nets

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