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During the status of real-time flight monitoring, it is related to the safety of flight that the accuracy of engine's rub-impact failure detection under the strong vibration environments. Under the strong vibration environment, when engine goes to stationary state, the engine's rub-impact failure is easy to happen, and they are often happened simultaneously. However, there is no correlation between...
An incremental association rules mining is one of an association rule mining research topics which finds the relation between set of item in dynamic databases. As data grows up rapidly, the co-occurrence itemset which discovered in the previous mining may be changed and the association rule will be change consequently. Incremental association rule mining research attempts to maintain that rules. Probability-based...
A decision support system based on data mining (DM) and Bayesian belief networks (BBN) is proposed to predict the student learning outcomes and takes the calculus course as an example to help students overcome their learning difficulties. Total of 427 freshmen in Ming Chi University of Technology (Taiwan) did questionnaires to assist this study. The methodologies involves four steps: fuzzy theory...
To address the problems of the rule redundancy and the long algorithm execution time in the process of mining one airborne radar intelligence database by the fuzzy association rules algorithm, this paper define a new QL-implicator based fuzzy support measure in order to enhance the recognition probability of the positive association rules and introduce the fuzzy conditional entropy measure (CE-measure)...
We consider the problem of applying probability concepts to discover frequent itemsets in a transaction database. The paper presents a probabilistic algorithm to discover association rules. The proposed algorithm outperforms the a priori algorithm for larger databases without losing a single rule. It involves a single database scan and significantly reduces the number of unsuccessful candidate sets...
An effective tracking method is proposed to solve the problem that the electro-optical tracking system in Missile Range easily loses the real target during the target separation. Before target separation, the error correcting value of the theoretical trajectory is obtained by the theoretical trajectory correcting algorithm. In the phase of target separation, the theoretical trajectory of the target...
This paper proposed a new idea based on adaptation technologies for the Web Interface. Web usage mining is used as a mission for mining personalized information from users. We try to attack the problem from both function objects in the interface and the interface region. It is helpful to design algorithms for finding frequent function objects pairs and a subregional set, which is based on association...
Privacy Preserving Data Mining (PPDM) is receiving a lot of attention recently by researchers from multiple domains, especially in Association Rule Mining. The outputs of Association Rule Mining often involve values of attributes that can be used to characterize the identities of the users. The relations between antecedents and consequents are also explicitly displayed. The purpose of preserving association...
With increasing Internet popularity, network security has become a serious problem recently. Therefore, a variety of algorithms have been devoted to this challenge. Genetic Network Programming is a newly developed evolutionary algorithm with directed graph gene structures, which has been applied to data mining for intrusion detection systems and has shown that it provides good performances in intrusion...
Many algorithms have been developed for rule mining in large transaction databases. Discovery of some important association rules is a main database mining problem. The objective of this study was to develop a new data mining algorithm named AKAMAS using different measures to extract the most important association rules for the assessment of heart event related risk factors. The implemented measures...
One application of data mining technology is to find the relationship of some product and sell appropriate product to appropriate customer at appropriate time. In order to applying data mining technology to help telecom companies find more cross-selling chances and carry out more available marketing measures to existing customers, an algorithm of quantitative association rule on fuzzy clustering is...
In this paper, we propose a novel rule deductive method to mine the real demanded association rules for any given user. This method does not like the most existing methods that mine frequent itemsets starting from candidate two-itemsets to candidate (n-1)-itemsets with inductive method and produce huge rough rules on these frequent itemsets. On the contrary, it avoids producing huge amounts of frequent...
Classification rule mining is a practical data mining technique widely used in real world. In the previous work, we have put forward a fuzzy class association rule mining method based on genetic network programming and applied it to network intrusion detection system which proved its efficiency and advantage. In this paper, a detailed comparison not only between fuzzy class association rule minings(FCARMs)...
In this paper, a novel evolutionary paradigm combining Genetic Network Programming (GNP) and Estimation of Distribution Algorithms (EDAs) is proposed and used to find important association rules in time-related applications, especially in traffic prediction. GNP is one of the evolutionary optimization algorithms, which uses directed-graph structures. EDAs is a novel algorithm, where the new population...
Searching statistically significant association rules is an important but neglected problem. Traditional association rules do not capture the idea of statistical dependence and the resulting rules can be spurious, while the most significant rules may be missing. This leads to erroneous models and predictions which often become expensive.The problem is computationally very difficult, because the significance...
Association rules is a technique that can detect patterns within the items of a dataset. The constrained version applies several restrictions that reduces the number of rules and also helps improve performance. On the other hand, OLAP statistical tests is an integration of exploratory On-Line Analytical Processing techniques and statistical tests. It uses a different approach that make it more appropriate...
Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy or correlativity respectively, even they ignore mining principal factors among all the correlativity. Thus, there seems a paradox among efficiency, accuracy and correlativity. In order to resolve to this conflict, a novel algorithm based on Probability estimate and principal component analysis...
We present a novel approach for assisting pattern interpretation by data mining end-users: finding explanations for association rules based on probabilistic dependencies. In the approach, relevant variables are selected from rules and from other data sources to facilitate human-understandable interpretations. An explanation of a rule involves consideration of observable variables in the data and alignment...
This paper has analysed the a priori algorithm performance, and has pointed out performance bottleneck question of the a priori algorithm. Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy respectively. There is a paradox between efficiency and accuracy. In order to resolve to this conflict, a novel algorithm based on probability estimate...
This paper presents an ACO-based (ant colony optimization) mining algorithm aiming to discover longer rule-chains directly. Firstly, a potential association rule directed graph (PAGraph) is created, in which, the dynamic heuristics is used to record participant-intensity of edge. Secondly, making use of ant's positive feedback, pheromone on edge that ants passed is adjusted by heuristics so that it...
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