Association rule mining is one of the most important and well-researched techniques of data mining, since the seminal papers by R. Agrawal et al. [1, 2]. It aims to induce associations among sets of items in the transaction databases or other data repositories. Ever since, several algorithms for specialized association tasks have appeared: quantitative association rules, generalized association...

We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy that says that jackets is-a outerwear is-e clothes, we may infer a rule that “people who buy outerw...

Mining sequential patterns only considers the sequential purchasing behaviors for most of the consumers. We can use this information to predict what products the consumers would like to purchase next time, but we cannot use this information to predict when the consumer will need the products in the future, that is we cannot know when the products need to be promoted to the consumers. In order t...

data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. it transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...

A large database, such as POS data, could give us many insights about customer behavior. Many techniques and measures have been proposed to extract the interesting rules. As the study of Association rule mining has proceeded, the rules about items that are not bought together at the same transaction have been regarded as important. Although this concept, Negative Association rule mining, is qui...

In many online shopping applications, traditional Association Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold. For example, those items that are put into the basket but not checked out. We say that those almost sold items carry hesitation information since customers are hesitating to buy them. The hesitation information o...

Discovery of association rules from large databases has been a focused topic recently in the research into database mining. Previous studies discover association rules at a single concept level, however, mining association rules at multiple concept levels may lead to nding more informative and re ned knowledge from data. In this paper, we study e cient methods for mining multiple-level associat...

In data mining, there are several works proposed for mining the association rules which are frequent. Researchers argue that mining the infrequent item sets are also important in certain applications. Discovering association rules are based on the preset minimum support threshold given by domain experts. The accuracy in setting up this threshold directly influences the number and the quality of...

It is a well accepted verity that the process of data mining produces numerous patterns from the given data. The most significant tasks in data mining are the process of discovering frequent itemsets and association rules. Numerous efficient algorithms are available in the literature for mining frequent itemsets and association rules. Incorporating utility considerations in data mining tasks is...

The effort of data mining, especially in relation to association rules in real world business applications, is significantly important. Recently, association rules algorithms have been developed to cope with multidimensional data. In this paper we are concerned with mining association rules in data warehouses by focusing on its measurement of summarized data. We propose two algorithms: HAvg and...