Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM). Because of the complexity of spatial data, a traditional method in extracting spatial association rules is to transform spatial database into general transaction database. The Apriori algorithm is one of the most commonly used methods in mining association rules at present. But a sh...

with the increased confidence in the use of the internet and the world wide web, the number of electronic commerce (e-commerce) transactions is growing rapidly. therefore, finding useful patterns and rules of users’ behaviors has become the critical issue for e-commerce and can be used to tailor e-commerce services in order to successfully meet the customers’ needs. this paper proposes an appro...

Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although there have been efforts for spatial association rule mining, but mostly researchers discuss only the positive spatial association rules; they have not considered the spat...

Classification rule mining is used to discover a small set of rules in the database to form an accurate classifier. Association rules mining are used to reveal all the interesting relationship in a potentially large database. For association rule mining, the target of the discovery is not predetermined, while for classification rule mining there is one and only one predetermined target. These t...

Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules, also called associative classification, is a technique that uses association rules to build classifier. CMAR employs a novel data structure, association rule, to compactly store and efficiently retrieve a ...

Association rule mining is an important topic in data mining research. Many algorithms have been developed for such task and they typically assume that the underlying associations hidden in the data are stable over time. However, in real world domains, it is possible that the data characteristics and hence the associations change significantly over time. Existing data mining algorithms have not...

Association rule mining is a computationally expensive task. Despite the huge processing cost, it is getting tremendous popularity due to the usefulness of the association rules. Several efficient algorithms can be found in the literature that cope with this popular task. This paper provides a comprehensive survey on the state-of-art algorithms for association rule mining, specially when the da...

An algorithm of constrained association rules mining was presented in order to search for some items expected by people. Since some presented algorithms of association rules mining based on binary are complicated to generate frequent candidate itemsets, they may pay out heavy cost when these algorithms are used to extract constrained spatial association rules. And so this paper proposes an algo...

Association Rule Mining is generally performed in Generation of frequent item sets & Rule generation. Mining association rules is not full of reward until it can be utilized to improve decision-making process of an organization. This paper is concerned with discovering positive and negative association rules. We present an Apriori-based algorithm that is able to find all valid positive and nega...