Association rule discovery has become one of the most widely applied data mining strategies. Techniques for association rule discovery have been dominated by the frequent itemset strategy as exemplified by the Apriori algorithm. One limitation of this approach is that it provides little opportunity to detect and remove association rules on the basis of relationships between rules. As a result, ...

How to efficiently discard potentially uninteresting rules in exploratory rule discovery is one of the important research foci in data mining. Many researchers have presented algorithms to automatically remove potentially uninteresting rules utilizing background knowledge and user-specified constraints. Identifying the significance of exploratory rules using a significance test is desirable for...

Because exploratory rule discovery works with data that is only a sample of the phenomena to be investigated, some resulting rules may appear interesting only by chance. Techniques are developed for automatically discarding statistically insignificant exploratory rules that cannot survive a hypothesis with regard to its ancestors. We call such insignificant rules derivative extended rules. In t...

Association rule discovery and other exploratory rule discovery techniques explore large search spaces of potential rules to find those that appear interesting by some user-selected criterion of interestingness. Due to the large number of rules considered, they suffer from an extreme risk of type-1 error, finding rules that appear to satisfy the interestingness criteria on the sample data only ...

Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules lead to another data mining problem, although of reduced complexity. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules to ultimately improve the overall efficiency of KDD process. In thi...

In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mini...

There are various advances in data collection that can intelligently and automatically analyze and mine knowledge from large amounts of data. World Wide Web as a global information system has flooded us with a tremendous amount of data and information Discovery of knowledge and decision-making directly from such huge volumes of data contents is a real challenge. The Knowledge Discovery in Datab...

|Although knowledge discovery is increasingly important in databases, discovered knowledge is not always useful to users. It is mainly because the discovered knowledge does not t user's interests, or it may be redundant or inconsistent with a priori knowledge. Knowledge discovery in databases depends critically on how well a database is characterized and how consistently the existing and discov...

ÐA top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more intere...

aims of religion are goals that the god perused in all of orders or large number of these. basis end of the aims of religion is proving the basis and general of religion to use of these to inference religious rules. it should be prove certitude of these aims to receive to this end. but because certitude of the religion aims direct relation to detection methods of their discovery, should be prov...