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Strong negative association rules can reveal irrelevances hidden between frequent itemsets. Existing research has made significant efforts in discovering both positive and negative association rules from single database. This paper presents an efficient method for mining strong negative association rules in multi-database. The method produces some strong negative relational patterns (a kind of infrequent...
Recently, mining negative association rules is an important research topic among various data mining problems and has been proved to be useful in real world. The issue of maintaining discovered negative association rules is paid more attention in the same way. Especially, the process of updating frequent negative itemsets is still a complicated issue for dynamic database that involve frequent additions...
Aiming at the prevalently concerned mining problem about constructing concept search in current Web search engine area, especially applying the vector space model VSM to Web search mining based on the association rules, this paper provides a highly efficient mining algorithm EARS. The EARS algorithm implements the association rules pruning based on VSM via constructing association library and computing...
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 data mining application to analysis of alarm correlation in the telecom network has been put out, the data mining algorithms of association rules and sequential patterns have been introduced, the comparing between two algorithms has been made, the analyzing & discussing of experimental result have been given with the alarming data of a certain network management center at Wuhan Telecom.
Aiming at the prevalently concerned mining problem about constructing concept search in current Web search engine area, especially applying the Vector Space Model VSM to Web search mining based on the association rules, this paper provides a highly efficient mining algorithm EARS. The EARS algorithm implements the association rules pruning based on VSM via constructing association library and computing...
Mining negative association rules in multi-database has attracted more and more attention. Most existing research focuses on unifying all negative rules discovered from different single databases into a single view. This paper presents a novel method for mining global negative association rules in multi-database. This method produces some infrequent itemsets of potential interest by scanning constructed...
Credible association rule(CAR) is a new type of association pattern in which items are highly affiliated with each other. The presence of an item in one transaction strongly implies the presence of every other item in the same CAR. And a maximal CAR is a CAR whose each superset isn't a CAR, so the maximal CAR specifies a more compact representation of a group of CARs. In this paper, we introduce some...
Mining sequential positive and negative association rules is to mine the inner association or the causal relationship among data in sequential database, which will find some rules that have practical significance for the industry decision-making analysis among the sequence. This paper proposes the relational notions of sequential positive and negative association rule. Based on the new questions when...
It is very necessary for e-marketers to understand e-shoppers' needs for making correct marketing strategies. Knowledge of e-shoppers purchase behaviors can be extracted from transaction databases. In this paper, transaction database is transformed into the database of linguistic values, and research on e-shopper purchase behavior is made based on linguistic values of product attributes. And an algorithm...
Because of the semantic gap between low-level feature and high-level semantic feature of images, the results of the traditional color-based image retrieval canpsilat meet userspsila needs. In order to eliminate interference factors in the image retrieval, use image semantic feature and improve the accuracy of image retrieval, the paper introduces an algorithm based on the color correlation mining...
BBS, which is constructed in Internet for providing public information to people, is one type of electric information system and social network. It also has the functions of sending message, email service etc. In this paper, we focus on the relations among users, boards and posts, especially by analyzing and mining history data. We have downloaded great deals of data from one forum, named New SMTH,...
Recently, mining negative association rules has received some attention and proved to be useful. Several algorithms have been proposed. However, there are some questions with those algorithms, for example, misleading rules will occur when the positive and negative rules are mined simultaneously. The chi-squared test can avoid the problem in the paper because of the mature theory basis. It is based...
In multi-database there are four category patterns which refer to frequent itemsets or association rules. Exception rules have been defined as rules with low support and high confidence. Exceptional patterns reflect the individuality of branches and provide valuable knowledge about database patterns, so it is very important to make special policies for these branches. For multi-database mining, gaining...
Data mining techniques have been developed in many applications. However, they also cause a threat to privacy. In this paper, we proposed a greedy method for hiding the number of sensitive rules. The experimental results showed that the undesired side effects can be avoided in the rule hiding process by use of our approach. The results also revealed that in most cases, all the sensitive rules are...
The every item is set a weight because there is different importance between items. Negative association rules become a focus in the field of data mining. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. The negative association rules often consist in the infrequent items. The negative rules mining...
Association rule mining is an important model in data mining. Many mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support and minimum confidence constraints. The weights are associated with the items to solve the question of different importance of the items. But there is another case that the frequency of every item is different from...
Existing algorithms that mine graph datasets to discover patterns corresponding to frequently occurring sub-graphs can operate efficiently on graphs that are sparse, contain a large number of relatively small connected components, have vertices with low and bounded degrees, and contain well-labeled vertices and edges. However, for graphs those do not share these characteristics, these algorithms become...
In this paper, a novel technique was present for mining complete correlation itemset, named as NLCD(non-linear correlation discovery) from hereon. In the first, it employ the vertical representation of a database, and then to find the direction between the correlative itemsets with fast processing and lots of them through the whole, including many kinds of correlation. Transaction ids of each itemset...
Recently, mining negative association rules has received some attention and been proved to be useful in real world. This paper presents an efficient algorithm (PNAR) for mining both positive and negative association rules in databases. The algorithm extends traditional association rules to include negative association rules. When mining negative association rules, we adopt another minimum support...
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