Network Data Mining and Analysis: The <$>{\cal {NEMESIS}}<$> Project p. 1 Privacy Preserving Data Mining: Challenges and Opportunities p. 13 Survey Papers (Invited) A Case for Analytical Customer Relationship Management p. 14 On Data Clustering Analysis: Scalability, Constraints, and Validation p. 28 Association Rules (I) Discovering Numeric Association Rules via Evolutionary Algori...

An interactive discovery method for finding association rules is presented. It consists in a user-guided search using reduction operators on a rule. Rules are generated on-demand according to the navigation made by the user. Main interest of this approach is that, at each step, the user has only a linear number of new rules to analyse and that all computations are done in polynomial time. Sever...

We consider the problem of clustering two-dimensional association rules in large databases. We present a geometricbased algorithm, BitOp, for performing the clustering, embedded within an association rule clustering system, ARCS. Association rule clustering is useful when the user desires to segment the data. We measure the quality of the segmentation generated by ARCS using the Minimum Descrip...

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm. We wil...

Many algorithms to mine the association rules are divided into two stages, the first is to find the frequent set; the second is use the frequent set to generate association rules. This proposal discuss the respective characteristics and .shortcoming of the current algorithms to mine association rules and propose another method to mine faster; unlike the other algorithms, this algorithm emphasis...

A manager would like to have a dashboard of his company without manipulating data. Usually, statistics have solved this challenge, but nowadays, data have changed (Jensen, 1992); their size has increased, and they are badly structured (Han & Kamber, 2001). A recent method—data mining—has been developed to analyze this type of data (Piatetski-Shapiro, 2000). A specific method of data mining, whi...

We study the problem of discovering association rules that display regular cyclic variation over time. For example, if we compute association rules over monthly sales data, we may observe seasonal variation where certain rules are true at approximately the same month each year. Similarly, association rules can also display regular hourly, daily, weekly, etc., variation that is cyclical in natur...

The focus of this paper is the discovery of negative association rules. Such association rules are complementary to the sorts of association rules most often encountered in literatures and have the forms of X→¬Y or ¬X→Y. We present a rule discovery algorithm that finds a useful subset of valid negative rules. In generating negative rules, we employ a hierarchical graph-structured taxonomy of do...

The current times have witnessed an exponential increase in the popularity of the Internet as well as advanced data collection tools across a wide variety of application domains. This has led to an explosive growth of data accumulation from terabytes to petabytes at a dramatic pace, and is already trending towards exabytes. The data flows from various sources including business (Web, e-commerce...

Value added product is an industrial term referring a minor addiction to some major products. In this paper, we borrow a term to denote a minor semantic addition to the well known association rules. We consider the addition of numerical values to the attribute values, such as sale price, profit, degree of fuzziness, level of security and so on. Such additions lead to the notion of random variab...