Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this subset the inform...

A thorough scrutiny of the literature dedicated to association rule mining highlights that a determined effort focused so far on mining the co-occurrence relations between items, i.e., conjunctive patterns. In this respect, disjunctive patterns presenting knowledge about complementary occurring items were neglected in the literature. Nevertheless, recently a growing number of works is shedding ...

9 ABSTRACT In a world where critical equipments are connected to internet, hence protection against professional cyber criminals is important. Today network security, uptime and performance of network are important and serious issue in computer network. Anomaly is deviation from normal behavior which is factor that affects on network security. So Anomaly Extraction which detects and extracts an...

A special challenge for spatial data mining is that information is not distributed uniformly in spatial data sets. Consequently, the discovery of regional knowledge is of fundamental importance. Unfortunately, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support in traditional association rule mining. Regional association rules, by definition, ...

The quality of rules generated by ontology-driven association rule mining algorithms is constrained by the algorithm’s effectiveness in exploiting the usually large ontology in the mining process. We present a framework built around superimposing a hierarchical graph structure on a given ontology to divide the rule mining problem into disjoint subproblems whose solutions can be iteratively join...

Association rule mining (ARM) aims at extraction, hidden relation, and interesting associations between the existing items in a transactional database. The purpose of this study is to highlight fundamental of association rule mining, association rule mining approaches to mining association rule, various algorithm and comparison between algorithms. Keywords—Assocation Rule Mining; Bottom up Appr...

Association Rule Mining (ARM) is one of the important data mining tasks that has been extensively researched by data-mining community and has found wide applications in industry. An Association Rule is a pattern that implies co-occurrence of events or items in a database. Knowledge of such relationships in a database can be employed in strategic decision making in both commercial and scientific...

The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery, that allow for the extraction of hidden knowledge in enormous...

This paper presents a hybrid, extensional and asymmetric matching approach designed to find out semantic relations (equivalence and subsumption) between entities issued from two textual taxonomies (web directories or OWL ontologies). By using the association rule paradigm and a statistical measure developed in this context, this method relies on the following idea: “An entity A will be more spe...