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With the rapid development of computer network technology, network not only provides the service for the people, but also has brought many negative effects. Intrusion detection is used to solve this problem. In order to improve the speed and intensity of intrusion detection, data mining technology can be applied to intrusion detection systems. Association rules are a common method in data mining....
The article analyzes the advantages and disadvantages of Apriori, AprioriTid and AprioriHybrid simply. To solve the bottleneck of AprioriHybrid algorithm, a kind of Hash-based method to condense candidate itemset is presented, and providing the corresponding algorithm. Finally the experiment proves the efficiency of improved AprioriHybrid-H is better than AprioriHybrid Algorithm's, there is strong...
Association rule mining based on support and confidence generates a large number of rules. However, post analysis is required to obtain interesting rules as many of the generated rules are useless. We pose mining association rules as multi-objective optimization problem where objective functions are rule interestingness measures and use NSGA-II, a well known multi-objective evolutionary algorithm...
Mining data streams for knowledge discovery is important to many applications, including Web click stream mining, network intrusion detection, and on-line transaction analysis. In this paper, by analyzing data characteristics, we propose an efficient algorithm SWSS (Sequential pattern mining with the weighted sliding window model in SPAM) to mine frequent sequential patterns based on the weighted...
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...
Document clustering is an important tool for applications such as search engines and document browsers. It enables the user to have a good overall view of the information contained in the documents. The well-known methods of document clustering, however, do not really address the special problems of text document clustering: very high dimensionality of the document, very large size of the datasets...
Every element of the transaction in a transaction database may contain the components such as item number, quantity, cost of the item bought and some other relevant information of the customer. Most of the association rules mining algorithms to discover frequent itemsets do not consider the components such as quantity, cost etc. In a large database it is possible that even if the itemset appears in...
Finding the large item set fast is the crucial step in the association rule algorithm. In this paper we apply granular computing and quotient space to frequent item set discovering , by partition the information system to information granule and mapping granule object sets, the algorithm reduced the number of database scanning and reduced object sets required when computing support of candidate item,...
With more important function in information society, software dependability has been in higher demand. Web application vulnerability has become one of the biggest threats for software security. Detecting and solving vulnerability is the effective way to enhance software dependability. Most active method traverses all Web links and interactive units in traversing step, which is easy to cause low efficiency...
Associative classification has high classification accuracy and strong flexibility. However, it still suffers from overfitting since the classification rules satisfied both minimum support and minimum confidence are returned as strong association rules back to the classifier. In this paper, we propose a new association classification method based on compactness of rules, it extends Apriori Algorithm...
The real-world data may be usually polluted by uncontrolled factors or contained with noisy. Fault-tolerant frequent pattern can overcome this problem. It may express more generalized information than frequent pattern which is absolutely matched. The present research is integrated with previous research into an integrity new method, called Top-NFTDS, to discover fault-tolerant association rules over...
Mining frequent patterns over streaming data has become an important research focus field with broad applications. However, the real-world data may be usually polluted by uncontrolled factors. Fault-tolerant frequent pattern can express more generalized information than frequent pattern which is absolutely matched. Therefore, a novel single-pass algorithm is proposed for efficiently mining top-k fault-tolerant...
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