Location-aware services using data mining techniques are recent research topics where rules from the data are extracted to provide interesting information. In addition, multi-agent systems are applied in location-based service for autonomous interaction of the system. Different data mining techniques are applied for knowledge discovery from location-based services. However, wireless environment...

Different studies have proposedmethods formining fuzzy association rules fromquantitative data, where themembership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extr...

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values and set the minimum supports and minimum confidences at numerical values. Linguistic minimum support and minimum confidence values are, however, more natural ...

In this paper we review the main intermediate forms proposed in text mining, and we briefly study some fuzzy counterparts. The concept of intermediate form applies to any knowledge representation employed to represent in a structured way the semantic content of a text corpus. Intermediate forms play a central role in the text mining process since it is necessary to transform plain text into a f...

In this paper, interactions among fuzzy, rough, and soft set theory has been studied. The authors have examined these theories as a problem solving tool in association rule mining problems of data mining and knowledge discovery in databases. Although fuzzy and rough set have been well studied areas and successfully applied in association rule mining problem, but soft set theory needs more atten...

General fuzzy association rules mining focuses on finding out the fuzzy itemsets or fuzzy attributes which frequently occur together. But two fuzzy itemsets which frequently occur together can not imply that there is always an interesting relationship between them. In this paper, we develop an alternative framework for mining interesting relationship between fuzzy itemsets based on fuzzy correl...

This paper is concerned with the data mining application to business process automation. A short tutorial on stages of financial analysis of an enterprise is given. A brief description of data mining components for financial analysis of an enterprise is presented in the paper. The main element of data mining application is fuzzy neural system. The structure of fuzzy neural system includes lingu...

Cloud computing are Internet-based services to increase the use and delivery models, usually involving the Internet to provide easy to dynamically scalable and often virtualized resources. To solve these problems, the proposed web data mining method based on cloud computing: the massive data mining tasks decomposed into parallel processing on multiple servers. Web fuzzy clustering of objects is...

Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffe...

In this paper we extend the problem of classification using Fuzzy Association Rule Mining and propose the concept of Fuzzy Weighted Associative Classifier (FWAC). Classification based on Association rules is considered to be effective and advantageous in many cases. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Weigh...