Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problem...

Association rule mining is the most popular technique in the area of data mining. The main task of this technique is to find the frequent patterns by using minimum support thresholds decided by the user. The Apriori algorithm is a classical algorithm among association rule mining techniques. This algorithm is inefficient because it scans the database many times. Second, if the database is large...

A huge amount of data is produced in our days in the agriculture sector. Due to the huge amount of this datasets it is necessary to use data mining techniques in order to comprehend the data and extract useful information. In our work we apply three different data mining techniques to data about Olea europaea var. media oblonga from the island of Thassos, at the northern part of Greece. The dat...

cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. coronary artery disease (cad) is the most common type of these diseases worldwide and is the main reason of heart attacks. thus early diagnosis of cad is very essential and is an important field of medical studies. many methods are used to diagnose cad so far. these methods reduce cost and...

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 ...

Data clustering is a process of putting similar data into groups. A clustering algorithms partition data set into several groups such that the similarity within a group is larger than among groups. Association rule is one of the possible methods for analysis of data. The association rules algorithm generates a huge number of association rules, of which many are redundant. The main idea of this ...

ÐWe discuss the problem of online mining of association rules in a large database of sales transactions. The online mining is performed by preprocessing the data effectively in order to make it suitable for repeated online queries. We store the preprocessed data in such a way that online processing may be done by applying a graph theoretic search algorithm whose complexity is proportional to th...

In the background of the information age, importance data resources can be imagined, and use means—data mining has also emerged. current situation, all industries are in a relatively equal stage, should make good resources, Apriori algorithm to mine association rules, formulate marketing strategies, promote sales growth and slow down loss national GDP. Countries also to make predictions ...

-Data mining includes number of techniques like clustering, classification, sequential patterns, association rules and etc. Association rule mining is a technique for mining interesting rules from large databases for further analysis. Association rule mining includes two-step approach for extracting the rules. These two steps require many database scan and use support and confidence as the thre...

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 given data set. This paper enables us to extract pattern from spatial database using k-means algorithm which refers to patterns not explicitly stored in spatial databases. Since spatial association mining...