Association rule mining is an important data mining problem. It is found to be useful for conventional relational data. However, previous work has mostly targeted on mining a single table. In real life, a database is typically made up of multiple tables and one important case is where some of the tables form a star schema. The tables typically correspond to entity sets and joining the tables in...

New application areas resulted in an increase of the diversity of the workloads that Data Base Management Systems have to confront. Resource management for mixed workloads is attained with the prioritization of their tasks, which during their execution may be forced to release some of their resources. In this paper, we consider workloads that consist of mixtures of OLTP transactions and associa...

In this paper we propose a method for extracting clusters in a population of customers, where the only information available is the list of products bought by the individual clients. We use association rules having high conndence to construct a hierarchical sequence of clusters. A speciic metric is introduced for measuring the quality of the resulting clusterings. Practical consequences are dis...

MusicBrainz is a publicly available relational database that stores information about artists, releases, tracks and the relationship among them. We present the results of mining association rules from this dataset, with the aim of obtaining knowledge about artists and their work. We are able to obtain associations between features, such as native language, and quantify how likely it is for an a...

The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and cor...

Data Mining is most commonly used in attempts to induce association rules from transaction data which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Most conventional studies are focused on binary or discrete-valued transaction data, however the data in real-world applications usually consists of quantitative values. In the last years, ...

This paper introduces a new algorithm called User Association Rules Mining (UARM) for solving the problem of generating inadequate large number of rules in mining association technique using a fuzzy logic method [1, 2]. In order to avoid user’s defined threshold mistakes, the user has flexibility to determine constraints based on a set of features. In comparison with other well-known and widely...

Association rule mining is considered as a Major technique in data mining applications. It reveals all interesting relationships, called associations, in a potentially large database. However, how interesting a rule is depends on the problem a user wants to solve. Existing approaches employ different parameters to guide the search for interesting rules. Class association rules which combine ass...

Association rule mining is a well established and popular data mining method for finding local dependencies between items in large transaction databases. However, a practical drawback of mining and efficiently using association rules is that the set of rules returned by the mining algorithm is typically too large to be directly used. Clustering association rules into a small number of meaningfu...

This paper shows a new method based on association rule mining and ontology for the classification of web pages. This work is pruning of association rules, generated by mining process. The main complexity arises due to the fact that there are various number of text documents that are considered for generating the association rules using the A-priori algorithm. But these rules that were generate...