web usage mining (wum) is the automatic discovering of hidden informationof user access pattern from the web log data. frequent pattern discovery is one ofthe main techniques in wum that can be used to implement recommender systems,forecast user`s navigational behaviour, and personalize web sites. many algorithmshave been suggested on obtaining frequent user navigation patterns. this paperprese...

The Internet has unlimited resources of knowledge and is widely used in many applications. Web mining plays an important role in discovering such knowledge, it is roughly divided into three categories : Web Content Mining , Web Usage Mining and Web Structure Mining. The web consists of imprecise, incomplete and uncertain data and knowledge. Fuzzy Set Theory is often used to handle such data. Se...

This paper surveys some genetic-fuzzy data mining techniques for mining both membership functions and fuzzy association rules. The motivation from crisp mining to fuzzy mining will be first described. Three types of genetic-fuzzy data mining approaches are then described according to the utilized methods and different mining problems, including Integrated GeneticFuzzy approaches for items with ...

Nowadays high fuzzy utility based pattern mining is an emerging topic in data mining. It refers to discover all patterns having a high utility meeting a user-specified minimum high utility threshold. It comprises extracting patterns which are highly accessed in mobile web service sequences. Different from the traditional fuzzy approach, high fuzzy utility mining considers not only counts of mob...

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Many types of knowledge and technology have been proposed for data mining. Among them, finding association rules from transaction data is most commonly seen. Most studies have shown how binary valued transaction data may be handled. Transaction data in real-world a...

AbsfracfData mining has a lot of e-Commerce applications. The key problem is how to tind useful hidden patterns for better business applications. For these problems, granular fuzzy Web intelligence techniques are used to implement the granular fuzzy Web data mining system for available historical data of the credit company customers. Fuzzy computing and granular computing are used to design the...

Fuzzy association rule mining (Fuzzy ARM) uses fuzzy logic to generate interesting association rules. These association relationships can help in decision making for the solution of a given problem. Fuzzy ARM is a variant of classical association rule mining. Classical association rule mining uses the concept of crisp sets. Because of this reason classical association rule mining has several dr...

Data mining, also called knowledge discovery in databases, is regarded as a non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable knowledge in large-scale data. This paper briefly reviews some typical applications and highlights potential contributions that fuzzy set theory can make to data mining. In this connection, some advantages of fuzzy methods...

The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and cor...

Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may also be represented in a fuzzy way. This paper thus proposes a fuzzy multiple-lev...