The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
An association rule (AR) is a common knowledge model in data mining that describes an implicative co-occurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent rArr consequent" rule. A variant of the AR is the weighted association rule (WAR). With regard to a marketing context, this paper introduces a...
The vertical association rules mining algorithm is an effective mining method recently, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantages that Apriori and its relative algorithms produce large amount of candidate itemsets and require scanning database many times. The vertical association rules mining algorithm needs...
Efficiency is critical to data mining algorithm. Based on fully analyzing the PF_growth, an association rule mining algorithm, we in this paper give a new association rule mining algorithm called MFP. MFP algorithm converts a transaction database to an MFP_tree through scanning the transaction database only once, then prune the tree and at last mine the tree. Because the MFP algorithm scans a transaction...
Discovering maximum frequent item sets is a key problem in data mining. In order to overcome the deficiencies of apriori-like algorithms which adopt candidate itemsets generation-and-test approach, we propose a new algorithm ML_DMFIA which based on DMFIA to mine maximum frequent itemsets in multiple-level association rules. ML_DMFIA utilizes FP-tree structure and up-down progressive deepening searching...
Because of its important application value in almost every region, early-warning has received extensive concern. This paper puts forward a study early-warning mechanism based on association rules. It uses an Apriori mining algorithm with some corresponding restrictions to dig out the latent school record association rules from former students' scores which are viewed as a history resource. Then these...
Due to the development of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of retail data in large databases. In the recent marketing research, products' discounts have rarely been considered as an important decision variable. Although few researches have analyzed the effect of discount on sales, they ignore its temporal characteristics. That...
Technology of frequent pattern tree is presented in the paper. This paper analyzes the defect and limitation of algorithm based on classic frequent pattern of association rules. Then based on KDD* model this article implement an association rules algorithms based on IFP-tree. The middle results and finally frequent patterns of the algorithm are stored on database. The algorithm in build IFP-tree and...
A mining top-n frequent closed itemsets of length no less than min_l algorithm is introduced by this paper, where n is the desired number of frequent closed itemsets to be mined, and min_l is the minimal length of each itemset. An efficient algorithm, called TFP, is developed for mining such itemsets without mins_support. Starting at min_support=0 and by making use of the length constraint and the...
Due to the recent competition in the retailing industry, retailers are striving to improve their operations in order to run their stores more efficiently. One of the most important factors that encourages customers to buy products is discount. The effects of discount on sales have rarely been dealt with academically. Moreover, in few previous researches in this case, the temporal characteristics of...
In this paper, we propose a new mining of frequent itemsets algorithm, called SFI-mine algorithm. The SFI-mine constructs pattern-base by using a new method which is different from the conditional pattern-base in FP-growth, mines frequent itemsets with a new combination method without recursive construction of conditional FP-trees. It obtains complete and correct frequent itemsets. We have conducted...
Mining association rules is an important field in data mining. The article discussed a graph-based association mining algorithm, which directly generate frequent candidate itemsets through constructing directed graphs to form association rules. But this algorithm occupy a great deal of time for checking the candidate itemsets, so an improved algorithm proposed. The improved algorithm utilize the method...
Association rule discovery is one of kernel tasks of data mining. Concept lattice, induced from a binary relation between objects and features, is a very useful formal analysis tool. It represents the unification of concept intension and extension. It reflects the association between objects and features, and the relationship of generalization and specialization among concepts. There is a one-to-one...
The discovery of association rules in data mining is an important issue, the core of which is the frequent pattern mining, Apriori algorithm is classical for the association rule mining, but it should repeatedly scan the database and can produce plenty of candidates. By examples, it is proved that Boolean matrix association rules algorithm can improve the algorithmic efficiency by reducing the times...
Sequential patterns mining is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which has two limitations. First, it can not concern quantitative attributes; second, only positive sequential patterns are discovered. Mining fuzzy sequential patterns has been proposed to address the...
In the Internet shopping environment, changes of customer's needs grow increasingly outstanding. For discovering the changes, the paper mines the transaction databases of different time periods by using association rule discovery, and extracts the association rules and discovers the changes in network customer behavior by comparison and analysis between the two sets of association rules. This paper...
Mining generalized association rules is one of important research area in data mining. If we use the traditional methods, it will meet two basic problems, the first is low efficiency in generating generalized frequent itemsets with the items and levels of taxonomy increasing, and the second is that too much redundant itemsets' support are counted. This paper proposes an improved Breadth-First Search...
Mining generalized association rules is closely related to the taxonomy(is-a hierarchy) data which exists widely in retail, geography, biology and financial domains. If we use traditional method to mine the generalized association rules, it becomes inefficient because the itemsets will be huge along with the items and levels of taxonomy increasing, and it also wastes lots of time to calculate the...
The classic association rules, Apriori algorithm and Fptree algorithm, are briefly illustrated to figure out the weakness of those algorithms. Then we develop an association rule tree algorithm from the idea of binary information granules. The algorithm retrieves the association rules from the association rule tree by computing of binary information granules, of which a system is converted from a...
Discovering association rules is one of the most important tasks in data mining. The classical model of association rules mining is support-confidence, the interestingness measure of which is the confidence measure. The classical Interestingness measure in Association Rules have existed some disadvantage. In this paper, some problem of interestingness measures on the classical association rules model...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.