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Quantitative Association Rule (QAR) mining has been recognized as an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. However, the combination of these quantitative attributes and their value intervals always rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we introduce a novel technique, called MFAMI, for mining quantitative association rules using fuzzy set theory. MFAMI employs linguistic terms to represent the revealed regularities and exceptions. This algorithm avoid the costly generation of a large number of candidate sets. Instead, using mutual information indicates the strong informative relationships among the attributes; potential frequent itemsets will be discovered. By utilizing those itemsets we devise an efficient algorithm that significantly generates rules. For effective mine rules, MFAMI employs adjusted difference analysis with this advantage that it does not require any user-supplied thresholds which are often hard to determine. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is capable of discovering meaningful and useful fuzzy association rules in an effective manner, speeding up the mining process and obtaining most of the high confidence QARs. [ABSTRACT FROM AUTHOR]

In recent years, various algorithms for mining closed frequent itemsets (CFI) have been proposed. Different structures such as prefix sharing are used within these algorithms. However the name challenging problem in many of them is the high requirement of memory, especially in case of sparse datasets. Thus, most of the proposed methods are proper for dense datasets. In this paper we present a new approach to mining closed frequent itemsets using two structures, namely Patricia tree and PTArray. By using these two structures, both the response time and also memory consumption are reduced, significantly. The proposed method, called PTclose is suitable for both dense and sparse datasets. The algorithm is assessed throughout a set of experiments. The results narrate for the relative efficiency of the algorithm compared to other existing methods. [ABSTRACT FROM AUTHOR]

Most of the proposed systems to process queries over web databases require the user to provide some information regarding the relative importance of attributes and the similarities between nominal values. Recently, a new system called AIMQ has been proposed, which is based on measuring concept similarities. This system is end-user independent and can answer imprecise queries. The main drawback of this system is that it is not incremental. All computations must be repeated when a tuple is added to the database. As a solution to this problem, in this article, we propose an incremental and efficient system called IQPI, which can be considered as the incremental version of AIMQ. In IQPI, the set of approximate dependencies between attributes are mined, first (using our new efficient approach). Using this set of dependencies, the user's imprecise query is converted into some precise queries. Each of the precise queries is then fed into the system and the results are filtered (to obtain most relevant answers) using concept similarity graphs. These graphs are constructed in another part of the system and each edge in a graph represents the similarity between two nominal values. The structure of the similarity graphs are such that the least amount of computation is needed for them to be updated, when database is changed. In dependency mining part of the system, we present a new incremental algorithm that is based on logical operations over bit strings. It is crucial for a search system to be incremental, due to the dynamic nature of the world-wide web. [ABSTRACT FROM AUTHOR]