An Improved Association Rule Algorithm With The Characteristic Of Dynamic Added Weight And Its Application In Fault Management Of Telecom

Data mining or Knowledge discovery in Database (KDD) is an merging field, which is to extract implicit, previously unknown, and potentially useful information out of large amounts of collected data . Mining association rule is an important content of data mining. Association is the discovery of association relationships or correlations among a set of items. They are always expressed in the rule form showing attribute-value conditions that occur frequently together in a given set of data . Mining association rule is a rule that all the support and confidence are more than minsup and minconf, given by customers, respectively. On the basis of traditional association rule algorithm, this text puts forward an Improved Association Rule Algorithm with the Characteristic of Dynamic Added Weight, then introducing its application in fault management of telecom. under the frame of traditional algorithm, each item is processed in equal and consistent way in the database, usually using the frequent degree (support) to measure their importance. However when the item of database are asymmetric distribution and have bigger discrepancy in the frequency of the appearance, it will cause hard enactment of minsup, if establish high, the association rules will may can not involve to item which appear the lower of frequency; But if establish low, will discover too many unmeaning association rules, may still cause combination explode. The association rule of added weight aim at the process of obtaining the association rule, and carry on the adjustment of this set of item's support. It mainly considers some circumstances of actuality, making some product which profit high rates but not trading frequent also can match the minimum support. Therefore need to promote the weight of those high value products, make them becoming the frequent item set, so as to find out its related rule. In order to weaken the influence of the subjective factor in the process of added weight, we adopt layer analysis method (AHP) which from a famous strategist of American A. L. Saaty puts forward to certain value of index weight, that method only need to ask expert to give comparison the importance between these two index sign, thus divide item of business database into five level according to its important degree. Make use of AHP to judge matrix, and then to calculate vector of characteristic; The vector that will get as the value of weight to compute heavy of weight of each other. After assigning the value of weight for each item, we make use of support of added weight that had gotten to construct the WFP _tree and to produce the item of frequent combination gradually. Be different from association rules algorithm of added weight under the APRIORI, we need to product frequent combination gradually by electing the candidate, then put the problem of frequent mode with long detection return to some short modes, then use the method of linking suffix to lower the expense for manhunt consumedly, all above just by building up a WFP _ tree. Under the environment of experiment that we built to make a detail comparison between the WFP_growth and the other traditional algorithm of association rule, we find that the conclusion of experiment and analysis of theories all show that this algorithm has a good characteristic of time and space. At the last part of this article, we combine the actual network environment of the telecommunication in jltele, putting forward model of warning sequence mode exhumation of the trouble of telecommunication. That model homologous carries on warning information which the network management software (OSSManager) had already collected , then combining to technique of glide window way to produce the business database , after that to circulate the algorithm of WFP_growth under this...