Parallel Semi-supervised EM-algorithm (MT-SSEM)

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

A Framework for Fast Classification Algorithms

Today, due to globalization of the world the size of data set is increasing, it is necessary to discover the knowledge. The discovery of knowledge can be typically in the form of association rules, classification rules, clustering, discovery of frequent episodes and deviation detection. Fast and accurate classifiers for large databases are an important task in data mining. There is growing evidence that integrating classification and association rules mining, classification approaches based on heuristic, greedy search like decision tree induction. Emerging associative classification algorithms have shown good promises on producing accurate classifiers. In this paper we focus on performance of associative classification and present a parallel model for classifier building. For classifier building some parallel-distributed algorithms have been proposed for decision tree induction but so far no such work has been reported for associative classification.

Fast Linear Algorithm for Active Rules Application in Transition P Systems

Transition P systems are computational models based on basic features of biological membranes and the observation of biochemical processes. In these models, membrane contains objects multisets, which evolve according to given evolution rules. In the field of Transition P systems implementation, it has been detected the necessity to determine whichever time are going to take active evolution rules application in membranes. In addition, to have time estimations of rules application makes possible to take important decisions related to the hardware / software architectures design. In this paper we propose a new evolution rules application algorithm oriented towards the implementation of Transition P systems. The developed algorithm is sequential and, it has a linear order complexity in the number of evolution rules. Moreover, it obtains the smaller execution times, compared with the preceding algorithms. Therefore the algorithm is very appropriate for the implementation of Transition P systems in sequential devices.

On Solving the Maximum Betweenness Problem Using Genetic Algorithms

In this paper a genetic algorithm (GA) is applied on Maximum Betweennes Problem (MBP). The maximum of the objective function is obtained by finding a permutation which satisfies a maximal number of betweenness constraints. Every permutation considered is genetically coded with an integer representation. Standard operators are used in the GA. Instances in the experimental results are randomly generated. For smaller dimensions, optimal solutions of MBP are obtained by total enumeration. For those instances, the GA reached all optimal solutions except one. The GA also obtained results for larger instances of up to 50 elements and 1000 triples. The running time of execution and finding optimal results is quite short.