Papers by Keyword: Association Rule

Paper TitlePage

Authors: Xiao Liang, Hong Wu Lv, Fang Fang Guo, Hui Qiang Wang
Abstract: Network Security Situation Awareness (NSSA) is a hot topic in network security field, and cloud computing is a new technology integrated virtual storage and distributed computing. It has become the challenging questions how to provide efficient and reliable service for NSSA based on the cloud computing.This paper proposes a cloud security situation awareness model based on data mining, and puts forwarda parallelfrequent-tree Apriori algorithm (PFT-Apriori) for mining association rules. Compare with the traditional Apriori algorithm, the experimental results show that the performance of system is increased by 51% under PFT-algorithm.
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Authors: Manoj Kumar, Hemant Kumar Soni
Abstract: Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.
120
Authors: Yu Xiang Song
Abstract: The alliance rules stated above based on the principle of data mining association rules provide a solution for detecting errors in the data sets. The errors are detected automatically. The manual intervention in the proposed algorithm is highly negligible resulting in high degree of automation and accuracy. The duplicity in the names field of the data warehouse has been remarkably cleansed and worked out. Domain independency has been achieved using the concept of integer domain which even adds on to the memory saving capability of the algorithm.
1878
Authors: Wen Liang Cao, Li Ping Chen
Abstract: Data mining has attracted a great deal of attention in the information industry in recent years and can be used for applications rangning from business management, production control, and science exploration etc. Most of the existing data mining algorithms are processing in the centralized systems; however, at present large database is usually distributed. Compared with the frequent itemsets lost and high communication traffic in distributed database conventional and improved algorithm FDM, An improved distributed data mining algorithm LTDM based on association roles is proposed. LTDM algorithm introduces the mapping indicated array mechanism to keep the integrity of frequent itemsets and decrease the communication traffic. The experimental results prove the efficiency of the proposed algorithm. The algorithm can be applied to information retrieval and so on in the digital library.
1459
Authors: Jiang Hui Cai, Wen Jun Meng, Zhi Mei Chen
Abstract: Data mining is a broad term used to describe various methods for discovering patterns in data. A kind of pattern often considered is association rules, probabilistic rules stating that objects satisfying description A also satisfy description B with certain support and confidence. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. The running results show that it is feasible and valuable to apply this method to mining the association rule and the improved algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm. Finally, the simulation results of knowledge acquisition for fault diagnosis also show the validity of CFP algorithm.
449
Authors: Zhi Feng Hao, Rui Chu Cai, Tang Wu, Yi Yuan Zhou
Abstract: Association rules provide a concise statement of potentially useful information, and have been widely used in real applications. However, the usefulness of association rules highly depends on the interestingness measure which is used to select interesting rules from millions of candidates. In this study, a probability analysis of association rules is conducted, and a discrete kernel density estimation based interestingness measure is proposed accordingly. The new proposed interestingness measure makes the most of the information contained in the data set and obtains much lower falsely discovery rate than the existing interestingness measures. Experimental results show the effectiveness of the proposed interestingness measure.
389
Authors: Jia Li Mao, Ming Dong Li, Min Liu
Abstract: In this paper we propose a new approach combines KNN method with FP-growth algorithm for identification and modeling existing dependencies between labels (ML-FKNN). We define and develop an algorithm that, first, utilize FP-growth algorithm for generating the association rules to identifies dependencies among the labels, then divides the whole train set into several mutually exclusive subsets to calculate the mean vectors of the each subset, and selects K nearest label neighbors for test instance by calculating its similarity with the mean vectors of the training subsets , and finally identifies the final predicted label set incorporating the discovered dependencies. Empirical evaluations on benchmark datasets shows that the proposed approach achieves high and stable accuracy results and is competitive with some existing methods for multi-label classification.
1554
Authors: Ping Shui Wang
Abstract: 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.
55
Authors: Liang Zhong Shen
Abstract: Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate professionals, association rule mining is receiving increasing attention. The technology of data mining is applied in analyzing data in databases. This paper puts forward a new method which is suit to design the distributed databases.
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Authors: Woon Ho Choi, Dong Keon Kim
Abstract: In this paper, the transmission and variation of tales between Yadamjip's was investigated. Yadamjip is a collection of Yadam, which is a tale of unofficial histories. The data was compiled from 12 books of Yadamjip and the number of tales used in this research is 2,144. The pairwise comparison of 2,144 tales to each other was committed and the transmission and variation of Yadamjip is inferred by computational clustering and text mining methods from the similarity of tales in each Yadamjip. Among the 12 Yadamjip's., it is revealed that there are three major categories of Yadamjip only with respect to the transmission relation. Especially, GIMUN (NL), GIMUM (YS), HAEDONG, CHEONGGU, DONGPAE, GYESEO were revealed to share various tales with trivial or minor variation.
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