Many data mining techniques consist in discovering patterns frequently occurring in the source dataset. Typically, the goal is to discover all the patterns whose frequency in the dataset exceeds a userspecified threshold. However, very often users want to restrict the set of patterns to be discovered by adding extra constraints on the structure of patterns. Data mining systems should be able to...

HealthObs is an integrated (Java-based) environment targeting the seamless integration and intelligent processing of distributed and heterogeneous clinical and genomic data. Via the appropriate customization of standard medical and genomic data-models HealthObs achieves the semantic homogenization of remote clinical and gene-expression records, and their uniform XML-based representation. The sy...

Mining for association rules is considered an important data mining problem. Many diierent variations of this problem have been described in the literature. In this paper we introduce the problem of mining for negative associations. A naive approach to nding negative associations leads to a very large number of rules with low interest measures. We address this problem by combining previously di...

Associative Classifier is a novel technique which is the integration of Association Rule Mining and Classification. The difficult task in building Associative Classifier model is the selection of relevant rules from a large number of class association rules (CARs). A very popular method of ordering rules for selection is based on confidence, support and antecedent size (CSA). Other methods are ...

This paper describes a proposal for enhanced visualization of a data-­‐mining model generated with Association Rule (AR) techniques by applying Self-­‐Organizing Maps (SOM). A representation of visual percep-­‐ tion model of AR based on a method called AVM-­‐DM (Augmented Visualiza-­‐ tion Models for Data Mining) is established, together with data and pat-­‐ terns, which support the visual expl...

Association Rule mining is one of the most popular data mining techniques which can be defined as extracting the interesting correlation and relation among large volume of transactions. E-commerce applications generate huge amount of operational and behavioral data. Applying association rule mining in e-commerce application can unearth the hidden knowledge from these data. In this paper a surve...

Closed itemset mining is a popular research in data mining. It was proposed to avoid a large number of redundant itemsets in frequent itemset mining. Various algorithms were proposed with efficient strategies to generate closed itemsets. This paper aims to study the existence algorithms used to mine closed itemsets. The various strategies in the algorithms are presented and analyzed in this paper.

Data warehouses store data that explicitly and implicitly reflect customer patterns and trends, financial and business practices, strategies, know-how, and other valuable managerial information. In this paper, we suggest a novel way of acquiring more knowledge from corporate data warehouses. Association-rule mining, which captures co-occurrence patterns within data, has attracted considerable e...

In retail organizations, a high volume of data is created through transactions made daily. To analyze these data it is necessary some kind of appropriate framework, environment or tool, and this is where KDD play its role. One classical KDD application is Association Rules [1], where elements are verified to check if their behavior is linked. In Market Basket, this is exactly what should be don...

Mining association rules is one of the most important tasks in data mining. The classical model of association rules mining is supportconfidence. The support-confidence model concentrates only on the existence or absence of an item in transaction records and does not take into account the products’ prices and quantities and how such these detailed information can affect the overall performance ...