Equipment quality-related data contains valuable information. Data mining technology seems to be an efficient method for extracting knowledge from large amounts of data. In this paper, a general equipment quality information based on association rule is proposed complex equipment. Due the shortcomings classical algorithms such as long running time and high memory consumption, candidate itemset ...

In order to quantify the transactions in investment,this paper establishes a non-subjective transaction strategy model 
 based on Apriori algorithm.The technical analysis index is blurred through triangular fuzzy device,designing decision system with class rule library,a product inference machine Mamdani meaning,and central average device.The structural parameters of are estimated using re...

Association rule mining finds interesting associations or correlations in a large pool of transactions. Apriori based algorithms are two step algorithms for mining association rules from large datasets. They find the frequent item sets from transactions as the first step and then construct the association rules. Though these algorithms generate multiple rules, most of the rules become irrelevan...

Today network security, uptime and performance of network are important and serious issues in computer network. Anomaly is deviation from normal behaviour affecting network security. Anomaly Extraction is identification of unusual flow from network, which is need of network operator. Anomaly extraction aims to automatically find the inconsistencies in large set of data observed during an anomal...

Finding association rules is an important data mining problem and can be derived based on mining large frequent candidate sets. In this paper, a new algorithm for efficient generating large frequent candidate sets is proposed, which is called Matrix Algorithm. The algorithm generates a matrix which entries 1 or 0 by passing over the cruel database only once, and then the frequent candidate sets...

Rsumm La ddcouverte des rgles d'association est l'un des principaux probllmes de l'extraction de connaissances dans les bases de donnnes. De nombreux algorithmes eecaces ont tt proposss, dont les plus remarquables sont Apriori, l'algorithme de Mannila, Partition, Sampling et DIC. Ces derniers sont tous basss sur la mmthode de recherche de Apriori: l''lagage du treillis des parties (treillis des...

The problem considered is that of finding frequent subpaths of a database of paths in a fixed undirected graph. This problem arises in applications such as predicting congestion in network traffic. An algorithm based on Apriori, called AFS, is developed, but with significantly improved efficiency through exploiting the underlying graph structure, which makes AFS feasible for practical input pat...

Rsumm La ddcouverte des rgles d'association est l'un des principaux probllmes de l'extraction de connaissances dans les bases de donnnes. De nombreux algorithmes eecaces ont tt proposss, dont les plus remarquables sont Apriori, l'algorithme de Mannila, Partition, Sampling et DIC. Ces derniers sont tous basss sur la mmthode de recherche de Apriori: l''lagage du treillis des parties (treillis des...