Traditional association rule mining consider support confident measures to find out frequent item sets, it assumes all items are having equal significance. Where as weighted association rule mining assigns weights to items based on different aspects. Because researchers are more concerned with qualitative aspects of attributes (e.g. significance), as compared to considering only quantitative on...

Weblogs are web sites where one or several authors publish their opinions about current events. Even in Spain, there are several thousands, and it is often difficult to find a weblog that meets one's interest. Recommendation services thus become, if not a need, at least a convenience. In this paper we propose automatic extraction of association rules from the results of a survey as a means to r...

This paper investigates a brute-force technique for mining classification rules from large data sets. We employ an association rule miner enhanced with new pruning strategies to control combinatorial explosion in the number of candidates counted with each database pass. The approach effectively and efficiently extracts high confidence classification rules that apply to most if not all of the da...

Association rules are one of the most researched areas of data mining and have recently received much attention from the database community. They have proven to be quite useful in the marketing and retail communities as well as other more diverse fields. In this paper we provide an overview of association rule research.

In this paper we present how to extract fuzzy association rules involving both the presence and the absence of items using a fuzzy rule mining procedure introduced by the authors in previous works. The rule mining procedure is based on the GUHA logical model, fuzzified via a recently proposed representation of gradualness. We present some results obtained with real datasets.

In this paper, an algorithm is proposed based on the concept of pre-large itemsets to maintain discovered generalized association rules for record modification. A pre-large itemset is not truly large, but promises to be large in the future. A lower and an upper support threshold are used to realize this concept. The two user-specified support thresholds make the pre-large itemsets act as a gap ...

Mining association rules is one of the most important tasks in data mining. The classical model of association rules mining is supportconfidence. The support-confidence model concentrates only on the existence or absence of an item in transaction records and does not take into account the products’ prices and quantities and how such these detailed information can affect the overall performance ...

Exploratory rule discovery, as exemplified by association rule discovery, is has proven very popular. In this paper I investigate issues surrounding the statistical validity of rules found using this approach and methods that might be employed to deliver statistically sound exploratory rule discovery.

Maintenance of association rules is an interesting problem. Several incremental maintenance algorithms were proposed since the work of (Cheung et al, 1996). The majority of these algorithms maintain rule bases assuming that support threshold doesn't change. In this paper, we present incremental maintenance algorithm under support threshold change. This solution allows user to maintain its rule ...

In this paper, we provide the basic concepts about association rule mining and compared existing algorithms for association rule mining techniques. Of course, a single article cannot describe all the algorithms in detailed, yet we tried to cover the major theoretical issues, which can help the researcher in their researches. KeywordsAssociation rules, algorithm, itemsets, database.