SemGrAM - Integrating Semantic Graphs into Association Rule Mining (original) (raw)

SWApriori: A New Approach to Mining Association Rules from Semantic Web Data

With the introduction and standardization of the semantic web as the third generation of the Web, this technology has attracted and received more human attention than ever and thus the amount of semantic web data is constantly growing. These semantic web data are a rich source of useful knowledge for feeding data mining techniques. Semantic web data have some complexities, such as the heterogeneous structure of the data, the lack of exactlydefined transactions, the existence of typed relationships between entities etc. One of the data mining techniques is association rule mining, the goal of which is to find interesting rules based on frequent item-sets. In this paper we propose a novel method that considers the complex nature of semantic web data and, without end-user involvement and any data conversion to traditional forms, mines association rules directly from semantic web datasets at the instance level. This method assumes that data have been stored in triple format (Subject, Predicate, and Object) in a single dataset. For evaluation purposes the proposed method has been applied to a drugs dataset that experiments results show the ability of the proposed algorithm in mining ARs from semantic web data without end-user involvement.

A novel model for mining association rules from semantic web data

2014 Iranian Conference on Intelligent Systems (ICIS), 2014

Nowadays, there is a continuous growth in the field of ontology and semantic annotations for numerous data of wide-ranging applications. This kind of heterogeneous and complex semantic data has created new challenges in the field of data mining research. An Association Rule Mining is one of the most common data mining techniques which can be well-defined for extracting the interesting relationships among the huge amount of transactions. Additionally, the Semantic Web technologies offer solutions to efficiently use the domain information. Hence this paper proposed a novel method to provide a way to address these issues and allow to process the huge volumes of semantic data. It executes association rule discovery to store the new semantic rules using the concept of semantic richness. It exist in the ontology and apply semantic technologies during all phases of the mining process. A novel method is proposed to efficiently extract items and transactions suited for traditional association rules mining algorithms.

Mining Association Rules with Ontological Information

Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), 2007

The problem of mining association rules incorporated with domain knowledge has been studied recently. Previous work was conducted individually on two types of knowledge, classification and composition. In this paper, we revisit this problem from a more unified viewpoint. We consider the problem of mining association rules with ontological information that presents not only classification but also composition relationship. Two effective algorithms are proposed with empirical evaluation displayed.

GARM: Generalized association rule mining

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 light on their importance for the sake of providing a richer knowledge for users. For this purpose, we propose in this paper a new tool, called GARM, aiming at building a partially ordered structure amongst some particular disjunctive patterns, namely the disjunctive closed ones. Starting from this structure, deriving generalized association rules, i.e., those offering conjunctive, disjunctive and negative connectors between items, becomes straightforward. Our experimental study put the focus on the mining performances as well as the quantitative aspect and proved the utility of the proposed approach.

GARM: A Simple Graph Based Algorithm For Association Rule Mining

International Journal of Computer Applications, 2013

Association rule mining is an important component of data mining. In the last years a great number of algorithms have been proposed with the objective of solving the obstacles presented in the generation of association rules. In this work, a new graph based algorithm for associative rule mining which has so many advantages over the existing methods is proposed. It can be used to improve decision making in a wide variety of applications such as: market basket analysis, medical diagnosis, bio-medical literature, protein sequences, census data, logistic regression, fraud detection in web, CRM of credit card business etc.

IJERT-Mining Association Rules Using Ontologies

International Journal of Engineering Research and Technology (IJERT), 2012

https://www.ijert.org/mining-association-rules-using-ontologies https://www.ijert.org/research/mining-association-rules-using-ontologies-IJERTV1IS7457.pdf Association rule mining is considered as one of the most important tasks in Knowledge Discovery in Databases. Among sets of items in transaction databases, it aims at discovering implicative tendencies that can be valuable information for the decision-maker. The rules generated by the existing methods are in more number. To reduce the number of rules several post processing methods and many techniques were developed but they are not effective. This paper aims to develop a new frame work called Mining Interest Rules Using Ontologies for extracting association rules based on user interest and also implementing a real time web semantic engine using an extended robust framework.

MRAR: Mining Multi-Relation Association Rules

Journal of Computing and Security

In this paper, we introduce a new class of association rules (ARs) named "Multi-Relation Association Rules" which in contrast to primitive ARs (that are usually extracted from multi-relational databases), each rule item consists of one entity and several relations. These relations indicate indirect relationship between entities. Consider the following Multi-Relation Association Rule where the first item consists of three relations live in, nearby and humid: "Those who live in a place which is near by a city with humid climate type and also are younger than 20 → their health condition is good". A new algorithm called MRAR is proposed to extract such rules from directed graphs with labeled edges which are constructed from RDBMSs or semantic web data. Also, the question "how to convert RDBMS data or semantic web data to a directed graph with labeled edges?" is answered. In order to evaluate the proposed algorithm, some experiments are performed on a sample dataset and also a real-world drug semantic web dataset. Obtained results confirm the ability of the proposed algorithm in mining Multi-Relation Association Rules.

An Improved Graph Based Method for Extracting Association Rules

International Journal of Software Engineering & Applications, 2015

This paper proposes an improved approach to mine strong association rules from an association graph, called graph based association rule mining (GBAR) method, where the association for each frequent itemset is represented by a sub-graph, then all sub-graphs are merged to determine association rules with high confidence and eliminate weak rules, the proposed graph based technique is self-motivated since it builds the association graph in a successive manner. These rules achieve the scalability and reduce the time needed to extract them. GBAR has been compared with three of the main graph based rule mining algorithms; they are, FP-Growth Graph algorithm, generalized association pattern generation (RIOMining) and multilevel association pattern generation (GRG). All of these algorithms depend on the construction of association graph to generate the desired association rules. On the other hand, this chapter expresses the observation results from the implementation of GBAR method recorded through the experiment. The detailed results are shown by different case studies in different minimum support thresholds values ranging from 90% down to 10% and minimum confidence values range from 55% to 95%. Generally, the observations focused on the execution time, the dimensionality of rules and the number of rules generated, because the performance of the association rule mining process affected directly of these criteria. Generally, the GBAR method has successfully reduced the execution time required to generate desired association rules in almost all of the dataset.

A Survey on Association Rule Mining

In recent years, Association Rule Discovery has become a core topic in Data Mining. It attracts more attention because of its wide applicability. Association rule mining is normally performed in generation of frequent itemsets and rule generation in which many researchers presented several efficient algorithms. This paper aims at giving a theoretical survey on some of the existing algorithms. The concepts behind association rules are provided at the beginning followed by an overview to some of the previous research works done on this area. The advantages and limitations are discussed and concluded with an inference.