Research on the Anti-risk Ability Intelligent Evaluation System of Distributed Data Centers without Human Intervention
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Using the association rules of the basic data of the whole link, the operation history law scenarios and the operation status risk perception methods among the distributed data centers are respectively studied. Modeling methods can be used to summarize the normal operation rules of various systems, and the operation rules can be used to find abnormal operation, and then determine the risk content. Its operation can be divided into different regularity stages to be distinguished, grasp the proportional relationship of each resource load in each stage, and realize the quantification of the regularity as a data index. Perform simulation tests to verify the effectiveness of the proposed method. Realize the quantification of abnormalities as data indicators, and then realize the conversion to risk judgments.In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. Aimed at two problems of discovering frequent itemsets in a large database and mining association rules from frequent itemsets, the author makes some research on mining frequent itemsets algorithm based on apriori algorithm and mining association rules algorithm based on improved measure system. Mining association rules algorithm based on support, confidence and interestingness is improved, aiming at creating interestingness useless rules and losing useful rules. Useless rules are cancelled, creating more reasonable association rules including negative items. The above method is used to mine association rules to the 2002 student score list of computer specialized field in Inner Mongolia university of science and technology.
Association (psychology)
Affinity analysis
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A challenging task in data mining is the process of discovering association rules from a large database. Most of the existing association rule mining algorithms make repeated passes over the entire database to determine the frequent itemsets, which is likely to incur an extremely high I/O overhead. A simple but an effective way to overcome this problem is to sample the database, such that, it produces rules with highest achievable accuracy on the large database. Numerous researchers have proposed sampling approaches for faster and efficient mining of association rules. In this paper, we propose a novel and effective progressive sampling-based approach for mining association rules from a large database. Initially, the frequent patterns are extracted using Apriori algorithm from an initial sample that is selected based on the temporal characteristics and the size of the database. Using the frequent itemsets generated, the negative border of the initial sample is obtained and sorted. Subsequently, the midpoint itemset in the sorted negative border is scanned in the concrete database to check if it is frequent. Based on the support level computed for the midpoint itemset, the sample size is either progressively increased for determining an optimal sample or association rules are mined by considering it as an optimal sample. The experimental results demonstrate the efficiency of the proposed progressive sampling approach in effective mining of association rules.
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Association rule mining was first introduced By Agarwalet al ([1], 1993; [3], 2000) which is the most important and well researched techniques ofdata mining, The Aimsof ARM to extract interesting correlations,repeated patterns, associations among sets of items in the transactiondatabases. Association rules are widely used in variousareas such as market,telecommunication networks,inventorycontrol and risk management etc. this paper leads the implementation of Apriori algorithm under the association rule mining technique of Data mining with using drought data. This algorithm is implemented with the help of Tangra([8], 2005) Data mining software.
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At present,the association rule mining focuses on Boolean association rule mining,few on quantitative association rules mining.Traditional methods are used to discrete quantify attribute,and then transform quantitative association rule mining to Boolean association rule mining.In order to overcome the interval divided excellent problem of traditional method,this paper designs the mining method of quantitative attribute association rules based on data field,and defines the formula for calculating support and confidence based on the quantitative of data field.This method is fully considered the incomplete of data in dataset and the different roles of each data for data mining,and makes association rules by mining accurate.
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Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Finding association rules can be derived based on mining large frequent candidate sets. Aiming at the poor efficiency of the classical Apriori algorithm which frequently scans the business database, studying the existing association rules mining algorithms, we proposed a new algorithm of association rules mining based on relation matrix. Theoretical analysis and experimental results show that the proposed algorithm is efficient and practical.
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Through the research on the existing data mining and association rules algorithms, for the deficiencies of the current association rule mining algorithm Apriori, this paper presents an improved Apriori association rule mining algorithm. The improved algorithm can reduce the number of generated candidate item sets and database scans through cutting of the frequent item set, thus greatly improving the efficiency of mining association rules.
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Traditional association mining often produces large numbers of association rules and sometimes it is very difficult for users to understand such rules and applying this knowledge to any business process. In order to overcome the drawback of association rule mining and to find actionable knowledge from resultant association rules, a novel idea of combined patterns is used here. Combined Mining is a kind of post processing method for association rules generated. In this approach, first the association rules are filtered by varying support and confidence levels, then using the interestingness measure Irule , association rules are further extracted. Here , the approach is applied on a survey dataset and the results prove that the method is very efficient than the traditional mining approach for obtaining actionable rules. The scheme of combined association rule mining can be extended for combined rule pairs and combined rule clusters. The efficiency can be further improved by the parallel implementation of this approach.
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K-optimal pattern discovery
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Association rules are a common method of data mining.This passage is based on the concept of frequent-item set in Apriori algorithm.After adding the concepts of meta-vector,sub rule,parent rule,we consider an improved mining method of association rules—Improve algorithm.This method conquers the disadvantage of traditional association rules mining methods,mining rules while mining frequent-item set,so the mining efficiency is greatly enhanced.
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Abstract The discovery of association rules is an important and challenging data mining task. Most of the existing algorithms for finding association rules require multiple passes over the entire database, and I/O overhead incurred is extremely high for very large databases. An obvious approach to reduce the complexity of association rule mining is sampling. In recent times, several sampling-based approaches have been developed for speeding up the process of association rule mining. A proficient progressive sampling-based approach is presented for mining association rules from large databases. At first, frequent itemsets are mined from an initial sample and subsequently, the negative border is computed from the mined frequent itemsets. Based on the support computed for the midpoint itemset in the sorted negative border, the sample size is either increased or association rules are mined from it. In this paper, we have presented an extensive analysis of the progressive sampling-based approach with different real life datasets and, in addition, the performance of the approach is evaluated with the well-known association rule mining algorithm, Apriori. The experimental results show that accuracy and computation time of the progressive sampling-based approach is effectively improved in mining of association rules from the real life datasets.
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Sample (material)
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Describes the field of data mining in the four commonly used data mining methods.In the data mining based on association rule mining,describes the classical association rule mining algorithm basis idea of Apriori algorithm.Through the experiment of association rule mining algorithm,gives the specific using method of the algorithm.Sums up the shortcomings of the algorithm.
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K-optimal pattern discovery
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