The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance corresponds to a certain kind of patte...

This work examines and proposes a new method for web mining inference based on Fuzzy Cognitive Maps. The web mining inference and knowledge extraction consists of two phases. At the first phase the a priori algorithm is used for web mining and a collection of Association rules is inferred. In the second phase, the set of Association Rules is transformed into a Fuzzy Cognitive Map (FCM). It inve...

Genetic fuzzy rule selection is a two-phase classification rule mining method. First a large number of candidate fuzzy rules are generated by an association rule mining technique. Then only a small number of generated rules are selected by a genetic algorithm. We have already proposed an idea of parallel distributed implementation of genetic fuzzy rule selection. In this paper, we examine its c...

This work extends fuzzy inference-grams (fingrams) to fuzzy association rules (FAR), yielding FARFingrams. Their analysis pays attention to interpretability issues. An important open problem in association rule mining is the huge number of frequent itemsets and interesting rules to uncover and communicate to the user. FAR-Fingrams address such problem through visual analysis. They ease the sele...

The high computational impact when mining fuzzy association rules grows significantly managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is these application of Big Data techniques can help achieve completion. Therefore, this paper several Spark algorithms are proposed handle with massive discover inte...

this paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein superfamily. since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. these features are the occurrence probabilities of six exchange groups in the seque...

While traditional algorithms concern positive associations between binary or quantitative attributes of databases, this paper focuses on mining both positive and negative fuzzy association rules. We show how, by a deliberate choice of fuzzy logic connectives, significantly increased expressivity is available at little extra cost. In particular, rule quality measures for negative rules can be co...

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...