We develop techniques for discovering patterns with periodicity in this work. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e., the periodicity. Periodicities in the dat...

Data mining has emerged to address the problem of drawing interesting knowledge from data. Among the most used data mining techniques, we concentrate on association rules which lead to the derivation of useful associations and correlations within data. In parallel, the advance of the ontology which is one of the most important concepts in knowledge representation has speedily altered the way of...

Recently, knowledge Discovery Process has proven to be a promising tool for extracting behavioral patterns regarding sensor nodes from wireless sensor networks. This paper presents a review of the available literature on the sensor association rules to find behavioral patterns between the sensor data.

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of...

Interestingness in Association Rules has been a major topic of research in the past decade. The reason is that the strength of association rules, i.e. its ability to discover ALL patterns given some thresholds on support and confidence, is also its weakness. Indeed, a typical association rules analysis on real data often results in hundreds or thousands of patterns creating a data mining proble...

Among the most powerful tools for knowledge representation, we cite the ontology which allows knowledge structuring and sharing. In order to achieve efficient domain knowledge bases content, the latter has to establish well linked and knowledge between its components. In parallel, data mining techniques are used to discover hidden structures within large databases. In particular, association ru...

This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among two parties involved in a data mining task. We study how to share private or confidential data in the following scenario: two parties, each having a private data set, want to collaboratively conduct association rule mining wit...

We have a large database consisting of sales transactions. We investigate the problem of online mining of association rules in this large database. We show how to preprocess the data e ectively in order to make it suitable for repeated online queries. The preprocessing algorithm takes into account the storage space available. We store the preprocessed data in such a way that online processing m...

We propose graph-pattern association rules (GPARs) for social media marketing. Extending association rules for itemsets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We study the problem of discovering topk diversified GPARs. While this problem is NP-hard, we develop a parallel algorithm with accuracy boun...

The growing advances in mobile devices, processing power, display and storage capabilities, together with competitive market has enabled information technology to be more affordable and available to almost everybody around the world. Moreover, with the advent of wireless communications and mobile computing, another type of wireless communications, called Mobile Ad hoc NETworks (MANETs), came in...