Lee, Stolfo, and Mok have previously reported the use of association rules and frequency episodes for mining audit data to gain knowledge for intrusion detection. The integration of association rules and frequency episodes with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in intrusion detection and security it...

Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. In this paper we present a new algorithm called SSDM (Semantically Similar Data Miner), which considers not only exact matches between items, but also the semantic similarity between them. SSDM uses fuzzy logic concepts to represent the similarity degree between item...

this paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. the classification performance andinterpretability are of major importance in these systems. in this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). ourapproach uses a punish...

The key issue of inventory management is the problem of safety stock control. The existence of imprecise data makes this control complex. Fuzzy logic (FL) is widely used to develop expert system, due to its ability in representing imprecise data. Therefore, in this study a fuzzy logic system and theory have been used that incorporate the linguistic variable more practically and also help in eli...

Association rule mining searches for interesting relationship among items in a large data set. Market basket analysis, a typical example of association rule mining, analyzes buying habit of customers by finding association between the different items that customers put in their shopping cart (basket). Apriori algorithm is an influential algorithm for mining frequent itemset for generating assoc...

A major problem when conducting mining fuzzy association rules from the database (DB) is the large computation time and memory needed. In addition, the selection of fuzzy sets for each attribute of the database is very important because it will affect the quality of the mining rule. This paper proposes a method for mining fuzzy association rules using compressed database. We also use the approa...

Abstract Online stores assist customers in buying the desired products online. Great competition e-commerce sector necessitates technology development. Many systems not only present but also offer similar to increase online customer interest. Due high product variety, analyzing sold together a recommendation system is must. This study methodologically improves traditional association rule minin...

The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discover...

Intrusion Detection is one of the important area of research. Our work has explored the possibility of integrating the fuzzy logic with Data Mining methods using Genetic Algorithms for intrusion detection. The reasons for introducing fuzzy logic is two fold, the first being the involvement of many quantitative features where there is no separation between normal operations and anomalies. Thus f...

Data mining is new but an interdisciplinary field utilizing statistics, machine learning, and other methods. In recent years, fuzzy logic has also been applied to augment data mining. The application of fuzzy logics makes the mining results more understandable and interpretable, apart from being useful and informative. Fuzzy rules are useful to summarize large databases. Several studies are don...