In the article the possibility of applying one of the methods of data mining – fuzzy association rules to model fuzzy systems is presented. The algorithm of automatic extracting knowledge base is a modification of the Apriori algorithm. The obtained rules of the model contain weights, that are calculated on the basis of measures of fuzzy association rules and constitute probability of fuzzy eve...

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. Transactions with quantitative values are however commonly seen in real-world applications. The fuzzy concepts are used to represent item importance, item qu...

In this chapter, we consider the application of generalization-based data mining to fuzzy similarity-based object-oriented databases (OODBs). Attribute generalization algorithms have been most commonly applied to relational databases, and we extend these approaches. A key aspect of generalization data mining is the use of a concept hierarchy. The objects of the database are generalized by repla...

Clustering is a data mining technique of grouping set of data objects into multiple groups or clusters so that objects within the cluster have high similarity, but are very dissimilar to the objects in the other clusters. Fuzzy C-Means is the most widely used method where an element may have partial membership grades in more than one fuzzy cluster. This paper makes use of MATLAB language to pro...

Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user’s current interest field from web log file. This paper presents web usage mining method for finding users’ current interest based on Fuzzy category. We consider not only how many times...

An improved fuzzy c-means algorithm is put forward and applied to deal with meteorological data on top of the traditional fuzzy c-means algorithm. The proposed algorithm improves the classical fuzzy c-means algorithm (FCM) by adopting a novel strategy for selecting the initial cluster centers, to solve the problem that the traditional fuzzy c-means (FCM) clustering algorithm has difficulty in s...

In today’s life Intrusion Detection System gain the attention, because of ability to detect the intrusion access efficiently and effectively as security is the major issue in networks. This system identifies attacks and reacts by generating alerts or blocking the unwanted data/traffic. Intrusion Detection System mainly classified as Anomaly based intrusion detection systems that have benefit of...

This study proposes design concepts for a comprehensive home financial learning environment that individual investors can use as a reference in establishing web-based learning and investment platforms. This study also introduces a hybrid approach that demonstrates a data mining function of the financial learning environment. Known as Fuzzy BPN, this approach is comprised of backpropagation neur...

Partitioning of quantified attributes is essential for mining association rules from quantified data and the Fuzzy approach solves the sharp boundary problem giving Fuzzy association rules having high interpretability and rich applicability. The paper presents automated partitioning of numerical data into Fuzzy sets based on k means clustering algorithm. This can be used as a pre-processing ste...

background: advances in information technology and data collection methods have enabled high-speed collection and storage of huge amounts of data. data mining can be used to derive laws from large data volumes and their characteristics. similarly, fuzzy logic by facilitating the understanding of events is considered a suitable complement to scientific data mining. materials and methods: the pre...