Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. Most conventional data-mining algorithms identify the relationships among transactions using binary values and 7nd rules at a single concept level. Transactions with quantitative values and items with hierarchy rela...

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...

The discovery of unexpected behaviors in databases is an interesting problem for many real-world applications. In previous studies, unexpected behaviors are primarily addressed within the context of patterns, association rules, or sequences. In this paper, we study the unexpectedness with respect to the fuzzy recurrence behaviors contained in sequence databases. We first propose the notion of f...

Association rule mining approaches traditionally generate rules based only on database contents, and focus on exact matches between items in transactions. In many applications, however, the utilization of some background knowledge, such as ontologies, can enhance the discovery process and generate semantically richer rules. Besides, fuzzy logic concepts can be applied on ontologies to quantify ...

Now a days, searching of specific type of knowledge from the usual standards is very useful in several domains such as medical diagnosis, fraud detection , network traffic anomalies, economic analysis etc. Fuzzy association rules have been developed as a powerful tool for dealing with imprecision in databases and offering a comprehensive representation of found knowledge. Adding fuzziness to no...

Global ocean salinity/temperature variations are attracting increasing attention, due to their influence on ocean-atmospheric changes and their potential for improved climate forecasting. The goal is to analyze historic salinity/temperature data to make predictions about future variations. Traditional statistical models that assume data independence are not applicable as ocean data are often in...

The definition of the Fuzzy Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. This paper discusses the results of two different hybrid methods, previously investigated, for the automatic generation of fuzzy rules from numerical data. One of the methods, named DoC-based, proposes the creation of Fuzzy Rule Bases using genetic algorithms in association with ...

Corresponding Author: Lekha. A., Research Scholar, Dr M G R Educational Research Institute, Chennai, India-600095, Assistant Professor, Department of MCA, PESIT, Bangalore Email: [email protected] Abstract: The paper attempts to propose a fuzzy logic association algorithm to predict the risks involved in identifying diseases like breast cancer. Fuzzy logic algorithm is used to find associatio...

Effective methods for feature and model structure selection are very important for data-driven modeling, data mining, and system identification tasks. This paper presents a new method for selecting important variables (regressors) in nonlinear (dynamic) models with mixed discrete (categorical, fuzzy) and continuous inputs and outputs. The proposed method applies fuzzy association rule mining. T...

This paper describes how to discover dynamic relationships among genes from time series microarray data with association rule mining approach. To hold dynamic information in the rules, the association rules were extracted using the constraints that expression level of genes appear in the antecedent and change direction of expression level of genes in the consequent. Besides, we have applied fuz...