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...

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 is a time consuming process due to involving both data intensive and computation intensive nature. In order to mine large volume of data and to enhance the scalability and performance of existing sequential association rule mining algorithms, parallel and distributed algorithms are developed. These traditional parallel and distributed algorithms are based on homogeneous ...

This paper addresses the problem of mining rank data, that is, data in the form of rankings (total orders) of an underlying set of items. More specifically, two types of patterns are considered, namely frequent subrankings and dependencies between such rankings in the form of association rules. Algorithms for mining patterns of this kind are proposed and illustrated on three case studies.

The research of spatial data is in its infancy stage and there is a need for an accurate method for rule mining. Association rule mining searches for interesting relationships among items in a

The results discussed in this paper are relevant to a large database consisting of consumer profile information together with behavioral (transaction) patterns. The focus of this paper is on the problem of onhne mining of profile association rules in this large database. The profile association rule problem is closely related to the quantitative association rule problem. We show how to use mult...

Mining frequent patterns in data is a useful requirement in several applications to guide future decisions. Association rule mining discovers interesting relationships among a large set of data items. Several association rule mining techniques exist, with the Apriori algorithm being common. Numerous algorithms have been proposed for efficient and fast association rule mining in data bases, but ...

Sequential pattern mining deals with data represented as sequences (a sequence contains sorted sets of items). Compared to the association rule problem, a study of such data provides “inter-transaction” analysis (Agrawal & Srikant, 1995). Applications for sequential pattern extraction are numerous and the problem definition has been slightly modified in different ways. Associated to elegant sol...

Narrowing down the computational space is a key factor in improving the efficiency of an association rule mining system. One approach to achieve this is to let the user guide the association rule mining process by enabling the user to specify the types of association rules that he/she might be interested in. Instead of computing all that can be computed, the system limits its association rule m...