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The generation of frequent itemsets is the key of association rules mining. Based on bit vectors and its intersection operation of the DLG ideas, this paper presents a new k-frequent itemsets generation algorithm based on bit matrix. The algorithm scans the database only once, using bit matrix of alternative association graph to store, constructing screening conditions to reduce the validation of...
This paper focus on the association rules which applied in data mining that aims to analyze large source data and reveal knowledge hidden in the database. The paper presents the principle of association rules in data mining. It has been viewed as an important evolution in information processing. This paper applies association rules mining to the software of examination paper evaluation system, obtaining...
Today, Internet has become an indispensable tool for everyone, Web usage mining correspondingly becomes a hotspot, which uses large amounts of data in the Web server log and other relevant data sets for mining analysis and gains valuable knowledge model about usage of relevant Web site. At present, a lot of works have to do with the positive association rules in Web usage mining, but negative association...
The problem of mining frequent itemsets plays an essential role in mining association rules, but it is not necessary to mine all frequent itemsets, instead it is sufficient to mine the set of frequent closed itemsets, which is much smaller than the set of all frequent itemsets. In this paper, we present an efficient algorithm, FCI-Miner, for mining all frequent closed itemsets. It based on the improved...
Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2-itemsets, the poor efficiency...
To solve the problem of mining weighted frequent traversal patterns (WFTPs) with noisy weight information from weighted directed graph (WDG), an effective algorithm called SWFTPMiner (statistical theory-based weighted frequent traversal patterns miner) is developed. It first adopts statistical notion called confidence interval (CI) to delete the vertices with noisy weights from the traversal database...
Traditional text mining techniques have weak ability to provide associated relations with rich semantics that is a foundation of the intelligent browsing of topics, discovery of semantic community and precise personalized recommendation in current Web and Knowledge Grid, etc. In this paper we propose an algorithm to generate and calculate the associated relations and their strengths between documents...
Recently, the topic of constraint based association mining has received increasing attention within the data mining research community. By allowing more user specified constraints other than traditional rule measurements, e.g., minimum support and confidence, research work on this topic endeavor to reflect real interest of analysts and relief them from the overabundance of rules, and ultimately, fulfill...
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