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...

Data mining is sorting through data to identify patterns and establish relationships. Association rule mining is a well established method of data mining that identifies significant correlations between items in transactional data. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining p...

Fuzzy set theory offers an important contribution to data mining leading to fuzzy data mining. It enables the management of interpretable and subjective information in both input and output of the data mining process. In this paper, we discuss the notion of interpretability in fuzzy data mining and we present some references on the management of emotions as a particular kind of subjective infor...

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...

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...

This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association can provide guidance assessing significant relationships that be determined while analyzing The approach uses cardinality sets a minimum and maximum range support confidence metrics. A new notation is used enable representation running example queries about desirable features vacat...

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...

One of the main concerns of an underground coal mining engineer is the safety and stability of the mine. One way that the safety and stability can be ensured is to know and understand the coal mine geology and how it reacts to the mining process. One technique that has shown a lot of success in the coal mining industry for geologic technical evaluation purposes is the coal mine roof rating (CMR...

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

There are various techniques for data mining and data analysis. Data mining is very important in the information retrieval areas especially when the data amounts are very large. Among them, hybrid approaches combining two or more algorithms gain importance as the complexity and dimension of real world data sets grows. In this paper, we present an application of evolutionary-fuzzy classification...