Traditional network intrusion detection algorithm is based on pattern matching, which has made great progress in network intrusion detection system, but the efficiency of this algorithm for data packet matching is qui...
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Traditional network intrusion detection algorithm is based on pattern matching, which has made great progress in network intrusion detection system, but the efficiency of this algorithm for data packet matching is quite low. With the rapid increase of Internet scale and capacity, the general information security problem appears, and it brought hidden danger for an open network security. In this paper, the author analyse the intrusion detection and performance simulation based on improved sequential pattern mining algorithm. We integrate the data miningalgorithms to implement the IDS, and the simulation result reflects the effectiveness of the methodology. The simulation shows that when minimum support is very small, PrefixSpan running time running a lot less time than other algorithm, and the difference between the two is obvious. Due to the miningalgorithm of the relative independence of intrusion detection system, algorithm does not depend on the specific data and specific system, so the intrusion detection system based on data mining to data source requirement is very low.
For many Markov chains that arise in applications (health, finance, etc.), state spaces are huge, and existing matrix methods may not be practical or even not possible to implement. In the literature, the expected wai...
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ISBN:
(纸本)9798350376975;9798350376968
For many Markov chains that arise in applications (health, finance, etc.), state spaces are huge, and existing matrix methods may not be practical or even not possible to implement. In the literature, the expected waiting time for Markov chain (with a smaller number of states) generated patterns are obtained by finding an appropriate pattern matrix and solving a set of linear equations. In this paper, a fuzzy transition probability (TP) matrix is introduced, and a data-driven fuzzy pattern mining algorithm is proposed for sequence data of any length. The proposed algorithm, which avoids the inversion of the pattern matrix, is applicable to Markov chains with huge state spaces. The proposed algorithm studies two examples involving DNA sequence data with 3954 base pairs and patterns generated by the log-returns of the stocks/cryptocurrencies. Expected weighting times are compared with the traditional matrix approach. Incorporating stochastic variation in the TP estimates through fuzzy matrices, the new approach provides an alternative path to produce a-cuts for TP matrices. The main contribution of this paper is to fit an appropriate MC model to a given sequence data and use the proposed fuzzy pattern mining algorithm to obtain resilient probabilistic forecasts and expected waiting time to reach patterns of interest.
In this paper, we present a new model for spatiotemporal saliency detection. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we pr...
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In this paper, we present a new model for spatiotemporal saliency detection. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we propose to apply the patternmining (PM) algorithm. From initial saliency maps computed in spatial and temporal domains, discriminative spatiotemporal saliency patterns can be recognized and their label information is propagated to obtain the final saliency map. Our model ensures a good compromise between image saliency and motion saliency and presents an accurate prediction to estimate salient regions in comparison with other methods for video saliency detection. Finally, as an application of our algorithm, our spatiotemporal saliency map is combined with appearance models and dynamic location models into an energy minimization framework to segment salient moving object. Experiments show a good performance of our algorithm for moving object segmentation (MOS) on benchmark datasets. (C) 2017 Elsevier Ltd. All rights reserved.
A new spatiotemporal saliency detection model is presented in this paper. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we propo...
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ISBN:
(纸本)9781509040629
A new spatiotemporal saliency detection model is presented in this paper. Instead of previous works which combine the image saliency in the spatial domain with motion cues to build their video saliency model, we propose to apply the pattern mining algorithm. From initial saliency maps computed in spatial and temporal domains, discriminative saliency patterns can be recognized and used to detect pertinent background and foreground seeds, then their label information is propagated to obtain the final saliency map. Our model ensures a good compromise between image saliency and motion saliency and presents an accurate prediction to estimate salient regions in comparison with other methods for video saliency detection.
Data stored in educational database is increasing day by day. Data miningalgorithms can be used to find hidden patterns from the student's database. These patterns can be used to find academic performance of stud...
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ISBN:
(纸本)9781479969296
Data stored in educational database is increasing day by day. Data miningalgorithms can be used to find hidden patterns from the student's database. These patterns can be used to find academic performance of students. The main aim of this study was to determine factors that influence the student's performance. This paper proposes Generalized Sequential pattern mining algorithm for finding frequent patterns from student's database and Frequent pattern tree algorithm to build the tree based on frequent patterns. This tree can be used for predicting the student's performance as pass or fail. Once the student is found at the risk of failure he/she can be provided guidance for performance improvement.
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