Lee, Stolfo, and Mok have previously reported the use of association rules and frequency episodes for mining audit data to gain knowledge for intrusion detection. The integration of association rules and frequency episodes with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in intrusion detection and security it...

Existing algorithms for mining association rule at multiple concept level, restricted mining strong association among the concept at same level of a hierarchy. However mining level-crossing association rule at multiple concept level may lead to the discovery of mining strong association among at different level of hierarchy. In this study, a top-down progressive deepening method is developed fo...

Association rule mining algorithm is used to extract relevant information from database and transmit into simple and easiest form. Association rule mining is used in large set of data. It is used for mining frequent item sets in the database or in data warehouse. It is also one type of data mining procedure. In this paper some of the association rule mining algorithms such as apriori, partition...

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the...

We present a method that applies association rule mining for information retrieval. Our approach is different from traditional information retrieval since retrieval is done based on association rather than similarity, which might be useful for knowledge discovery purposes such as finding an explanation or elaboration for an event in a collection of domain-specific documents. The method proposed...

Web usage mining is the application of data mining techniques to discover usage patterns from web data. It can be used to better understand web usage and better serve the needs of rapidly growing web-based applications. Discovery of browsing patterns, page clusters, user clusters, association rules and usage statistics are some usage patterns in the web domain. Web mining of browsing patterns i...

A variety of data mining techniques are under evaluation on the spatial data of concern in our setting. We are planning to integrate a number of these techniques into our geospatial system (GIDB). Three approaches are under special consideration and are described in the paper. A COTS data mining system has been successfully used to develop predictive models of near-shore conditions such as wave...

One of the best-known problems in data mining is association rule mining. It requires very large computation and I/O traffic capacity, therefore several distributed and parallel association rule mining algorithms have been developed. However the association rule mining problem is NP complete, the execution time estimation of the algorithms can be very important, especially for load balancing or...

This paper surveys some genetic-fuzzy data mining techniques for mining both membership functions and fuzzy association rules. The motivation from crisp mining to fuzzy mining will be first described. Three types of genetic-fuzzy data mining approaches are then described according to the utilized methods and different mining problems, including Integrated GeneticFuzzy approaches for items with ...

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to si...