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Mining high utility item sets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although several studies have been carried out, current methods present the mined results in the form of textual lists for users, which degrades the understand ability of the discovered...
Recently, high utility itemsets mining becomes one of the most important research issues in data mining due to its ability to consider different profit values for every item. In the past studies, most algorithms generate high utility itemsets from a set of transactions in horizontal data format. Inspired by the problem of frequent itemset mining, vertical mining may be a promising approach superior...
Most previous sequential mining algorithms have the following two main drawbacks: On one hand, all sequential patterns are treated uniformly while sequential patterns have different importance. On the other hand, most of the sequence mining algorithms still generate an exponentially large number of sequential patterns when a minimum support is lowered. In this paper, a weighted closed sequential pattern...
The application of association rule mining to classification has led to a new family of classifiers which are often referred to as Associative Classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable...
It is well-recognized that the main factor that hinders the applications of association rules (ARs) is the huge number of ARs returned by the mining process. To solve this problem, an algorithm for mining concise association rules based on generators and closed itemsets is proposed. Firstly, the concept of concise association rule is proposed, and the rationality of the definition is explained based...
The application of association rule mining to classification has led to a new family of classifiers which are often referred to as associative classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable...
With the rapid development of the network technique and the prevalence of the Internet, e-learning has become the major trend of the development of international education since 1980s, and the important access for the internationalization and the information of education. To meet the personalized needs of learners in e-learning, a new Web text clustering method for personalized e-learning based on...
How to reduce the number of frequent itemsets effectively is a hot topic in data mining research. Clustering frequent itemsets is one solution to the problem. Since generators are lossless concise representations of all frequent itemsets, clustering generators is equivalent to clustering all frequent itemsets. This paper proposes a new algorithm for clustering frequent itemsets based on generators...
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