Utility mining is to find the itemsets in a transaction database with high utility values like profits. Although a number of algorithms on high utility mining have been proposed, they did not reflect the fuzzy degree of quantity and profit level for mined high utility itemsets, which are essential for decision making in various applications like stock control and sales analysis. In this paper, ...

A fuzzy sequential pattern consisting of several fuzzy sets represents a frequently occurring behavior related to time and can be discovered from transaction bases. An example is that large purchase amounts of one product were bought by customers after these consumers had bought small purchase amounts of another product. Recently, Hu et al. (2003) proposed a fuzzy data mining method to discover...

Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very imp...

Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of...

Association rule mainly focuses on large transactional databases. In association rule mining all items are considered with equal weightage. But it is not suitable for all datasets. The weight should be considered based on the importance of the item. In our previous work HITS algorithm (Hyperlink Induced Topic Search) is used to find the weight of an item w-support is calculated for generating f...

Association rule mining has wide variety of research in the field of data mining, many of association rule mining approaches are well investigated in literature, but the major issue with ARM is, huge number of frequent patterns cannot produce direct knowledge or factual knowledge, hence to find factual knowledge and to discover inference, we propose a novel approach AFIRM in this paper followed...

Aiming at high overshoot and steady-state error in fuzzy controller of underwater vehicles, a new method based on data mining technique was presented. Apply Boolean association rule data mining to mine the polling list of fuzzy control from the database of manual operation record, and simulation and outfield experiments were carried out on Super Mohawk underwater vehicle to verify the feasibili...

Data mining tools generally deal with highly structured and precise data. However, classical methods fail to handle imprecise or uncertain information. This paper proposes a neuro-fuzzy data mining approach which provides a means to deal with the uncertainty of data. This presents a location-based service collaboration framework and uses the neuro-fuzzy algorithm for data mining. It also introd...

the use of fuzzy techniques has been considered to be one of the key components of data mining systems because of the affinity with human knowledge representation. A hybridization of fuzzy sets with genetic algorithms is described for Web mining in this paper. It is based on a hybrid technique that combines the strengths of rough set theory and genetic algorithm. The algorithm through the intro...

Machine learning, data mining, and several related research areas are concerned with methods for the automated induction of models and the extraction of interesting patterns from empirical data. Automated knowledge acquisition of that kind has been an essential aspect of artificial intelligence since a long time and has more recently also attracted considerable attention in the fuzzy sets commu...